Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering
In this paper, we formulate a hierarchical Bayesian version of the Mixture of Unigrams model for text clustering and approach its posterior inference through variational inference. We compute the explicit expression of the variational objective function for our hierarchical model under a mean-field...
Saved in:
| Published in | Computational statistics Vol. 38; no. 4; pp. 2015 - 2051 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0943-4062 1613-9658 1613-9658 |
| DOI | 10.1007/s00180-023-01350-8 |
Cover
| Abstract | In this paper, we formulate a hierarchical Bayesian version of the Mixture of Unigrams model for text clustering and approach its posterior inference through variational inference. We compute the explicit expression of the variational objective function for our hierarchical model under a mean-field approximation. We then derive the update equations of a suitable algorithm based on coordinate ascent to find local maxima of the variational target, and estimate the model parameters through the optimized variational hyperparameters. The advantages of variational algorithms over traditional Markov Chain Monte Carlo methods based on iterative posterior sampling are also discussed in detail. |
|---|---|
| AbstractList | In this paper, we formulate a hierarchical Bayesian version of the Mixture of Unigrams model for text clustering and approach its posterior inference through variational inference. We compute the explicit expression of the variational objective function for our hierarchical model under a mean-field approximation. We then derive the update equations of a suitable algorithm based on coordinate ascent to find local maxima of the variational target, and estimate the model parameters through the optimized variational hyperparameters. The advantages of variational algorithms over traditional Markov Chain Monte Carlo methods based on iterative posterior sampling are also discussed in detail. |
| Author | Bilancia, Massimo Manca, Fabio Di Nanni, Michele Pio, Gianvito |
| Author_xml | – sequence: 1 givenname: Massimo orcidid: 0000-0002-5330-2403 surname: Bilancia fullname: Bilancia, Massimo email: massimo.bilancia@uniba.it organization: Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Policlinic University Hospital – sequence: 2 givenname: Michele surname: Di Nanni fullname: Di Nanni, Michele organization: EY Business and Technology Solution – sequence: 3 givenname: Fabio surname: Manca fullname: Manca, Fabio organization: Department of Education, Psychology, Communication (ForPsiCom), University of Bari Aldo Moro, Palazzo Chiaia - Napolitano – sequence: 4 givenname: Gianvito surname: Pio fullname: Pio, Gianvito organization: Department of Computer Science, University of Bari Aldo Moro |
| BookMark | eNqNkMtKAzEUhoNUsK2-gKsB19FcZzJLrVcouFG3IZPJ2JS5mWSwfXvTTkFwUVwdOPzf4T_fDEzarjUAXGJ0jRHKbjxCWCCICIUIU46gOAFTnGIK85SLCZiinFHIUErOwMz7NUKEZARPgfxQzqpgu1bVyZ3aGp8YH2yzXyVdlaysccrpldUxcG-d1avaBNgMdbBt19i4bewmDC6SVeeSYDYh0fXgg3G2_TwHp5Wqvbk4zDl4f3x4WzzD5evTy-J2CTVNaYCMUYpzw7jGNDYtCCsLnqamUCY1XBRlXjFWlIqWgnNTaMI4w4QLlmWsJFrQOaDj3aHt1fZb1bXsXXzDbSVGcudIjo5kdCT3juSOuhqp3nVfQ3xcrrvBRRVeEiHyLI9tcEyRMaVd570z1f9Oiz-QtmFvNThl6-Po4Rff7xQa99vqCPUDI8KawA |
| CitedBy_id | crossref_primary_10_1016_j_joi_2024_101633 crossref_primary_10_1186_s40537_024_00930_9 crossref_primary_10_1007_s11135_022_01460_3 crossref_primary_10_1109_ACCESS_2024_3385628 |
| Cites_doi | 10.1214/07-AOAS114 10.1007/978-3-319-16181-5_39 10.1201/9780429055911 10.1007/s11222-014-9500-2 10.1111/1467-9868.00265 10.1007/s11222-011-9236-1 10.2307/2288938 10.18637/jss.v050.i10 10.1145/183422.183423 10.1080/01621459.2017.1285773 10.1080/00437956.1954.11659520 10.1038/nature14541 10.1007/978-1-4614-3223-4 10.1093/imanum/draa038 10.1145/2133806.2133826 10.1017/CBO9780511809071 10.1093/biomet/49.1-2.65 10.1561/2200000001 10.1214/06-BA121 10.1109/TPAMI.2018.2889774 10.1145/1143844.1143892 10.1023/A:1007665907178 10.1023/A:1007612920971 10.1080/03610926.2021.1921214 10.1007/978-3-030-51310-8_10 10.1023/A:1007692713085 10.1007/s11634-020-00399-3 10.1093/biostatistics/kxy018 10.1111/j.2517-6161.1994.tb01985.x 10.1007/978-0-387-84858-7 10.1007/978-0-387-35768-3 10.1111/j.1368-423X.2004.00125.x 10.1007/0-387-71599-1 10.3390/e22111263 10.1080/01621459.2000.10474285 10.18637/jss.v025.i05 10.1007/s11222-015-9561-x 10.1007/s40745-015-0040-1 10.1201/b17520 10.1080/10618600.2014.983643 10.1201/b16018 10.1214/20-EJS1756 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| DBID | AAYXX CITATION 3V. 7SC 7TB 7WY 7WZ 7XB 87Z 88I 8AL 8C1 8FD 8FE 8FG 8FK 8FL 8G5 ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FR3 FRNLG FYUFA F~G GHDGH GNUQQ GUQSH HCIFZ JQ2 K60 K6~ K7- KR7 L.- L6V L7M L~C L~D M0C M0N M2O M2P M7S MBDVC P5Z P62 PHGZM PHGZT PJZUB PKEHL PPXIY PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PTHSS Q9U ADTOC UNPAY |
| DOI | 10.1007/s00180-023-01350-8 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Science Database (Alumni Edition) Computing Database (Alumni Edition) Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) Research Library Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Database ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Engineering Research Database Business Premium Collection (Alumni) Health Research Premium Collection ABI/INFORM Global (Corporate) Health Research Premium Collection (Alumni) ProQuest Central Student Research Library Prep SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database (Proquest) Civil Engineering Abstracts ABI/INFORM Professional Advanced ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Research Library ProQuest Science Database Engineering Database (Proquest) Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection ProQuest Central Basic Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) Research Library Prep Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ABI/INFORM Complete ProQuest One Applied & Life Sciences Health Research Premium Collection Health & Medical Research Collection ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global Engineering Database ProQuest Science Journals (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection Health Research Premium Collection (Alumni) ProQuest Business Collection ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Research Library (Alumni Edition) ProQuest Central ABI/INFORM Professional Advanced ProQuest Health & Medical Research Collection ProQuest Engineering Collection ProQuest Central Korea ProQuest Research Library Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) Civil Engineering Abstracts ProQuest Computing ProQuest Public Health ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Mathematics |
| EISSN | 1613-9658 |
| EndPage | 2051 |
| ExternalDocumentID | oai:ricerca.uniba.it:11586/429445 10_1007_s00180_023_01350_8 |
| GroupedDBID | -5D -5G -BR -EM -Y2 -~C .86 .VR 06D 0R~ 0VY 199 1N0 203 29F 2J2 2JN 2JY 2KG 2LR 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 53G 5GY 5VS 67Z 6NX 78A 7WY 88I 8C1 8FE 8FG 8FL 8G5 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADBBV ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BAPOH BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC FYUFA GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HLICF HMJXF HQYDN HRMNR HVGLF HZ~ H~9 IHE IJ- IKXTQ ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV L6V LAS LLZTM M0C M0N M2O M2P M4Y M7S MA- MK~ N2Q N9A NB0 NPVJJ NQJWS NU0 O9- O93 O9J OAM P2P P62 P9R PF0 PQBIZ PQBZA PQQKQ PROAC PT4 PTHSS Q2X QOS R89 R9I RNS ROL RPX RSV S16 S1Z S27 S3B SAP SDH SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TSK TSV TUC U2A UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7X Z7Y Z81 Z83 Z88 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB PUEGO 7SC 7TB 7XB 8AL 8FD 8FK FR3 JQ2 KR7 L.- L7M L~C L~D MBDVC PKEHL PQEST PQUKI Q9U ADTOC UNPAY |
| ID | FETCH-LOGICAL-c363t-443319e45c13943b24db566ebae6e58bd9f44bda3d855ebc245412584774d2c83 |
| IEDL.DBID | UNPAY |
| ISSN | 0943-4062 1613-9658 |
| IngestDate | Sun Oct 26 04:14:22 EDT 2025 Fri Jul 25 19:22:07 EDT 2025 Wed Oct 01 05:00:55 EDT 2025 Thu Apr 24 23:03:00 EDT 2025 Fri Feb 21 02:41:47 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | Text clustering Finite mixture models Variational inference Dirichlet-multinomial distribution Bayesian hierarchical modelling |
| Language | English |
| License | cc-by-nc-nd |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c363t-443319e45c13943b24db566ebae6e58bd9f44bda3d855ebc245412584774d2c83 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0002-5330-2403 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://hdl.handle.net/11586/429445 |
| PQID | 2889791391 |
| PQPubID | 54096 |
| PageCount | 37 |
| ParticipantIDs | unpaywall_primary_10_1007_s00180_023_01350_8 proquest_journals_2889791391 crossref_primary_10_1007_s00180_023_01350_8 crossref_citationtrail_10_1007_s00180_023_01350_8 springer_journals_10_1007_s00180_023_01350_8 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20231200 2023-12-00 20231201 |
| PublicationDateYYYYMMDD | 2023-12-01 |
| PublicationDate_xml | – month: 12 year: 2023 text: 20231200 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationTitle | Computational statistics |
| PublicationTitleAbbrev | Comput Stat |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | Zhang C, Kjellström H (2015) How to supervise topic models. In: Agapito L, Bronstein MM, Rother C (eds) Computer vision—ECCv 2014 workshops. Springer, Cham, pp 500–515. https://doi.org/10.1007/978-3-319-16181-5_39 R Core Team (2022) R: a language and environment for statistical computing. https://www.R-project.org Nielsen F, Garcia V (2009) Statistical exponential families: a digest with flash cards. arXiv:0911.4863 TitteringtonDMWangBConvergence properties of a general algorithm for calculating variational Bayesian estimates for a Normal mixture modelBayesian Anal2006222129110.1214/06-BA1211331.62168 ManningCDRaghavanPSchützeHIntroduction to information retrieval2008CambridgeCambridge University Press10.1017/CBO97805118090711160.68008 CeleuxGFrüwirth-SchnatterSRobertCPFrühwirth-SchnatterSCeleuxGRobertCPModel selection for mixture models—perspectives and strategiesHandbook of mixture analysis2018New YorkChapmann & Hall11815410.1201/9780429055911 AnderlucciLViroliCMixtures of Dirichlet-multinomial distributions for supervised and unsupervised classification of short text dataAdv Data Anal Classif202014759770420157210.1007/s11634-020-00399-31459.62102 CeleuxGHurnMRobertCPComputational and inferential difficulties with mixture posterior distributionsJ Am Stat Assoc200095957970180445010.1080/01621459.2000.104742850999.62020 PolliceABilanciaMA hierarchical finite mixture model for Bayesian classification in the presence of auxiliary informationMetron Int J Stat2000LVIII10913118542250999.62504 Greene D, Cunningham P (2006) Practical solutions to the problem of diagonal dominance in kernel document clustering. In: Proceedings of the 23rd international conference on machine learning (ICML’06). ACM Press, pp 377–384 AggarwalCCZhaiCMining text data2012New YorkSpringer10.1007/978-1-4614-3223-4 BlanchardPHighamDJHighamNJAccurately computing the log-sum-exp and softmax functionsIMA J Numer Anal20214123112330432838510.1093/imanum/draa0381509.65019 HastieTTibshiraniRFriedmanJThe elements of statistical learning20092New YorkSpringer10.1007/978-0-387-84858-71273.62005 KeribinCConsistent estimation of the order of mixture modelsSankhyā Indian J Stat Ser A (1961–2002)200062496617697351081.62516 XuDTianYA comprehensive survey of clustering algorithmsAnn Data Sci2015216519310.1007/s40745-015-0040-1 MarinJMRobertCApproximating the marginal likelihood in mixture modelsIndian Bayesian Soc Newslett2008527 van der MaatenLHintonGVisualizing data using t-SNEJ Mach Learn Res20089257926051225.68219 AnastasiuDCTagarelliAKarypisGAggarwalCCReddyCKDocument clustering: the next frontierData clustering: algorithms and applications2014Boca RatonChapman & Hall305338 BaudryJPMaugisCMichelBSlope heuristics: overview and implementationStat Comput201222455470286502910.1007/s11222-011-9236-11322.62007 SankaranKHolmesSPLatent variable modeling for the microbiomeBiostatistics201920599614401972010.1093/biostatistics/kxy018 Frühwirth-SchnatterSEstimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniquesEconom J20047143167207663010.1111/j.1368-423X.2004.00125.x1053.62087 Malsiner-WalliGFrühwirth-SchnatterSGrünBModel-based clustering based on sparse finite Gaussian mixturesStat Comput201626303324343937510.1007/s11222-014-9500-21342.62109 ZhangCButepageJKjellstromHAdvances in variational inferenceIEEE Trans Pattern Anal Mach Intell2019412008202610.1109/TPAMI.2018.2889774 DaytonCMMacreadyGBConcomitant-variable latent-class modelsJ Am Stat Assoc19888317394101410.2307/2288938 JordanMIGhahramaniZJaakkolaTSAn introduction to variational methods for graphical modelsMach Learn19993718323310.1023/A:10076659071780945.68164 Silverman J (2022) RcppHungarian: solves minimum cost bipartite matching problems. https://CRAN.R-project.org/package=RcppHungarian, R package version 0.2 FeinererIHornikKMeyerDText mining infrastructure in RJ Stati Softw200810.18637/jss.v025.i05 Kaggle (2022) Sports dataset(bbc). https://www.kaggle.com/datasets/maneesh99/sports-datasetbbc. Accessed 04 Nov 2022 BleiDMKucukelbirAMcAuliffeJDVariational inference: a review for statisticiansJ Am Stat Assoc2017112859877367177610.1080/01621459.2017.1285773 Frühwirth-SchnatterSFinite mixture and Markov switching models2006New YorkSpringer10.1007/978-0-387-35768-31108.62002 KunkelDPeruggiaMAnchored Bayesian Gaussian mixture modelsElectron J Stat2020416549610.1214/20-EJS17561452.62453 LiHFanXA pivotal allocation-based algorithm for solving the label-switching problem in Bayesian mixture modelsJ Comput Graph Stat201625266283347404710.1080/10618600.2014.983643 Andrews N, Fox E (2007) Recent developments in document clustering. http://hdl.handle.net/10919/19473, Virginia Tech computer science technical report, TR-07-35 StephensMDealing with label switching in mixture modelsJ R Stat Soc Ser B (Stat Methodol)200062795809179629310.1111/1467-9868.002650957.62020 CeleuxGKamaryKMalsiner-WalliGFrühwirth-SchnatterSCeleuxGRobertCPComputational solutions for Bayesian inference in mixture modelsHandbook of mixture analysis2018New YorkChapmann & Hall7311510.1201/9780429055911 GelmanACarlinJSternHBayesian data analysis20133Boca RatonChapman and Hall10.1201/b160180914.62018 Maechler M (2022) Rmpfr: R mpfr—multiple precision floating-point reliable. https://cran.r-project.org/package=Rmpfr, R package version 0.8-9 Feinerer I, Hornik K (2020) tm: text mining package. https://CRAN.R-project.org/package=tm, R package version 0.7-8 RakibMRHZehNJankowskaMMétaisEMezianeFHoracekHEnhancement of short text clustering by iterative classificationNatural language processing and information systems2020BerlinSpringer10511710.1007/978-3-030-51310-8_10 WallachHMimnoDMcCallumABengioYSchuurmansDLaffertyJRethinking LDA: why priors matterAdvances in neural information processing systems2009New YorkCurran Associates Inc. RobertCPThe Bayesian choice2007New YorkSpringer10.1007/0-387-71599-1 WainwrightMJJordanMIGraphical models, exponential families, and variational inferenceFound Trends® Mach Learn20071130510.1561/22000000011193.62107 HornikKFeinererIKoberMSpherical k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}-means clusteringJ Stat Softw201210.18637/jss.v050.i10 Tran MN, Nguyen TN, Dao VH (2021) A practical tutorial on variational Bayes. arXiv:2103.01327 BaudryJPCeleuxGEM for mixtures. Inizialiation requires special careStat Comput201525713726336048710.1007/s11222-015-9561-x1331.62301 BleiDMNgAYJordanMILatent Dirichlet allocationJ Mach Learn Res2003399310221112.68379 Chandra NK, Canale A, Dunson DB (2020) Escaping the curse of dimensionality in Bayesian model based clustering. arxiv:2006.02700 HarrisZSDistributional structureWORD19541014616210.1080/00437956.1954.11659520 BleiDMLaffertyJDA correlated topic model of scienceAnn Appl Stat2007239383910.1214/07-AOAS1141129.62122 BleiDMProbabilistic topic modelsCommun ACM201255778410.1145/2133806.2133826 MosimannJEOn the compound multinomial distribution, the multivariate Beta-distribution, and correlations among proportionsBiometrika196249658214329910.1093/biomet/49.1-2.650105.12502 DieboltJRobertCPEstimation of finite mixture distributions through Bayesian samplingJ R Stat Soc Ser B (Methodol)199456363375128194010.1111/j.2517-6161.1994.tb01985.x0796.62028 LeeSYGibbs sampler and coordinate ascent variational inference: a set-theoretical reviewCommun Stat Theory Methods202110.1080/03610926.2021.1921214 PlummerSPatiDBhattacharyaADynamics of coordinate ascent variational inference: a case study in 2D Ising modelsEntropy2020221263422206610.3390/e22111263 GhahramaniZProbabilistic machine learning and artificial intelligenceNature201552145245910.1038/nature14541 NigamKMccallumAKThrunSText classification from labeled and unlabeled documents using EMMach Learn20003910313410.1023/A:10076927130850949.68162 AiroldiEMBleiDEroshevaEAHandbook of mixed membership models and their applications2014Boca RatonChapman and Hall10.1201/b175201369.62003 MurphyKPMachine learning: a probabilistic perspective2012CambridgeThe MIT Press1295.68003 AptéCDamerauFWeissSMAutomated learning of decision rules for text categorizationACM Trans Inf Syst19941223325110.1145/183422.183423 AwasthiPRisteskiACortesCLawrenceNLeeDOn some provably correct cases of variational inference for topic modelsAdvances in neural information processing systems2015New YorkCurran Associates, Inc. DhillonISModhaDSConcept decompositions for large sparse text data using clusteringMach Learn20014214317510.1023/A:10076129209710970.68167 Nikita M (2020) ldatuning: tuning of the latent Dirichlet allocation models parameters. https://CRAN.R-project.org/package=ldatuning, R package version 1.0.2 DM Blei (1350_CR14) 2017; 112 DM Blei (1350_CR13) 2003; 3 SY Lee (1350_CR36) 2021 G Celeux (1350_CR16) 2018 1350_CR46 D Xu (1350_CR60) 2015; 2 1350_CR44 1350_CR49 K Sankaran (1350_CR52) 2019; 20 S Frühwirth-Schnatter (1350_CR25) 2006 MJ Wainwright (1350_CR58) 2007; 1 ZS Harris (1350_CR29) 1954; 10 M Stephens (1350_CR54) 2000; 62 DM Blei (1350_CR12) 2007 D Kunkel (1350_CR35) 2020 1350_CR5 L Anderlucci (1350_CR4) 2020; 14 CP Robert (1350_CR51) 2007 S Frühwirth-Schnatter (1350_CR24) 2004; 7 CD Manning (1350_CR40) 2008 G Celeux (1350_CR17) 2018 P Blanchard (1350_CR10) 2021; 41 A Gelman (1350_CR26) 2013 1350_CR33 1350_CR38 Z Ghahramani (1350_CR27) 2015; 521 DC Anastasiu (1350_CR3) 2014 C Apté (1350_CR6) 1994; 12 JM Marin (1350_CR41) 2008; 5 C Keribin (1350_CR34) 2000; 62 KP Murphy (1350_CR43) 2012 1350_CR62 1350_CR23 A Pollice (1350_CR48) 2000; LVIII G Celeux (1350_CR15) 2000; 95 1350_CR28 L van der Maaten (1350_CR57) 2008; 9 JP Baudry (1350_CR8) 2015; 25 JP Baudry (1350_CR9) 2012; 22 K Nigam (1350_CR45) 2000; 39 P Awasthi (1350_CR7) 2015 MI Jordan (1350_CR32) 1999; 37 DM Blei (1350_CR11) 2012; 55 T Hastie (1350_CR30) 2009 G Malsiner-Walli (1350_CR39) 2016; 26 H Wallach (1350_CR59) 2009 H Li (1350_CR37) 2016; 25 DM Titterington (1350_CR55) 2006 EM Airoldi (1350_CR2) 2014 J Diebolt (1350_CR21) 1994; 56 K Hornik (1350_CR31) 2012 1350_CR53 IS Dhillon (1350_CR20) 2001; 42 1350_CR56 CC Aggarwal (1350_CR1) 2012 1350_CR18 MRH Rakib (1350_CR50) 2020 C Zhang (1350_CR61) 2019; 41 CM Dayton (1350_CR19) 1988; 83 I Feinerer (1350_CR22) 2008 JE Mosimann (1350_CR42) 1962; 49 S Plummer (1350_CR47) 2020; 22 |
| References_xml | – reference: FeinererIHornikKMeyerDText mining infrastructure in RJ Stati Softw200810.18637/jss.v025.i05 – reference: Maechler M (2022) Rmpfr: R mpfr—multiple precision floating-point reliable. https://cran.r-project.org/package=Rmpfr, R package version 0.8-9 – reference: Zhang C, Kjellström H (2015) How to supervise topic models. In: Agapito L, Bronstein MM, Rother C (eds) Computer vision—ECCv 2014 workshops. Springer, Cham, pp 500–515. https://doi.org/10.1007/978-3-319-16181-5_39 – reference: BaudryJPMaugisCMichelBSlope heuristics: overview and implementationStat Comput201222455470286502910.1007/s11222-011-9236-11322.62007 – reference: Chandra NK, Canale A, Dunson DB (2020) Escaping the curse of dimensionality in Bayesian model based clustering. arxiv:2006.02700 – reference: HarrisZSDistributional structureWORD19541014616210.1080/00437956.1954.11659520 – reference: PlummerSPatiDBhattacharyaADynamics of coordinate ascent variational inference: a case study in 2D Ising modelsEntropy2020221263422206610.3390/e22111263 – reference: AwasthiPRisteskiACortesCLawrenceNLeeDOn some provably correct cases of variational inference for topic modelsAdvances in neural information processing systems2015New YorkCurran Associates, Inc. – reference: Nikita M (2020) ldatuning: tuning of the latent Dirichlet allocation models parameters. https://CRAN.R-project.org/package=ldatuning, R package version 1.0.2 – reference: MarinJMRobertCApproximating the marginal likelihood in mixture modelsIndian Bayesian Soc Newslett2008527 – reference: ZhangCButepageJKjellstromHAdvances in variational inferenceIEEE Trans Pattern Anal Mach Intell2019412008202610.1109/TPAMI.2018.2889774 – reference: BleiDMLaffertyJDA correlated topic model of scienceAnn Appl Stat2007239383910.1214/07-AOAS1141129.62122 – reference: KunkelDPeruggiaMAnchored Bayesian Gaussian mixture modelsElectron J Stat2020416549610.1214/20-EJS17561452.62453 – reference: AptéCDamerauFWeissSMAutomated learning of decision rules for text categorizationACM Trans Inf Syst19941223325110.1145/183422.183423 – reference: RakibMRHZehNJankowskaMMétaisEMezianeFHoracekHEnhancement of short text clustering by iterative classificationNatural language processing and information systems2020BerlinSpringer10511710.1007/978-3-030-51310-8_10 – reference: DhillonISModhaDSConcept decompositions for large sparse text data using clusteringMach Learn20014214317510.1023/A:10076129209710970.68167 – reference: StephensMDealing with label switching in mixture modelsJ R Stat Soc Ser B (Stat Methodol)200062795809179629310.1111/1467-9868.002650957.62020 – reference: JordanMIGhahramaniZJaakkolaTSAn introduction to variational methods for graphical modelsMach Learn19993718323310.1023/A:10076659071780945.68164 – reference: R Core Team (2022) R: a language and environment for statistical computing. https://www.R-project.org/ – reference: Kaggle (2022) Sports dataset(bbc). https://www.kaggle.com/datasets/maneesh99/sports-datasetbbc. Accessed 04 Nov 2022 – reference: Nielsen F, Garcia V (2009) Statistical exponential families: a digest with flash cards. arXiv:0911.4863 – reference: Silverman J (2022) RcppHungarian: solves minimum cost bipartite matching problems. https://CRAN.R-project.org/package=RcppHungarian, R package version 0.2 – reference: LeeSYGibbs sampler and coordinate ascent variational inference: a set-theoretical reviewCommun Stat Theory Methods202110.1080/03610926.2021.1921214 – reference: GelmanACarlinJSternHBayesian data analysis20133Boca RatonChapman and Hall10.1201/b160180914.62018 – reference: van der MaatenLHintonGVisualizing data using t-SNEJ Mach Learn Res20089257926051225.68219 – reference: DaytonCMMacreadyGBConcomitant-variable latent-class modelsJ Am Stat Assoc19888317394101410.2307/2288938 – reference: XuDTianYA comprehensive survey of clustering algorithmsAnn Data Sci2015216519310.1007/s40745-015-0040-1 – reference: AggarwalCCZhaiCMining text data2012New YorkSpringer10.1007/978-1-4614-3223-4 – reference: SankaranKHolmesSPLatent variable modeling for the microbiomeBiostatistics201920599614401972010.1093/biostatistics/kxy018 – reference: TitteringtonDMWangBConvergence properties of a general algorithm for calculating variational Bayesian estimates for a Normal mixture modelBayesian Anal2006222129110.1214/06-BA1211331.62168 – reference: WainwrightMJJordanMIGraphical models, exponential families, and variational inferenceFound Trends® Mach Learn20071130510.1561/22000000011193.62107 – reference: AnastasiuDCTagarelliAKarypisGAggarwalCCReddyCKDocument clustering: the next frontierData clustering: algorithms and applications2014Boca RatonChapman & Hall305338 – reference: BaudryJPCeleuxGEM for mixtures. Inizialiation requires special careStat Comput201525713726336048710.1007/s11222-015-9561-x1331.62301 – reference: BlanchardPHighamDJHighamNJAccurately computing the log-sum-exp and softmax functionsIMA J Numer Anal20214123112330432838510.1093/imanum/draa0381509.65019 – reference: AiroldiEMBleiDEroshevaEAHandbook of mixed membership models and their applications2014Boca RatonChapman and Hall10.1201/b175201369.62003 – reference: KeribinCConsistent estimation of the order of mixture modelsSankhyā Indian J Stat Ser A (1961–2002)200062496617697351081.62516 – reference: Frühwirth-SchnatterSFinite mixture and Markov switching models2006New YorkSpringer10.1007/978-0-387-35768-31108.62002 – reference: DieboltJRobertCPEstimation of finite mixture distributions through Bayesian samplingJ R Stat Soc Ser B (Methodol)199456363375128194010.1111/j.2517-6161.1994.tb01985.x0796.62028 – reference: RobertCPThe Bayesian choice2007New YorkSpringer10.1007/0-387-71599-1 – reference: HastieTTibshiraniRFriedmanJThe elements of statistical learning20092New YorkSpringer10.1007/978-0-387-84858-71273.62005 – reference: BleiDMProbabilistic topic modelsCommun ACM201255778410.1145/2133806.2133826 – reference: CeleuxGHurnMRobertCPComputational and inferential difficulties with mixture posterior distributionsJ Am Stat Assoc200095957970180445010.1080/01621459.2000.104742850999.62020 – reference: CeleuxGKamaryKMalsiner-WalliGFrühwirth-SchnatterSCeleuxGRobertCPComputational solutions for Bayesian inference in mixture modelsHandbook of mixture analysis2018New YorkChapmann & Hall7311510.1201/9780429055911 – reference: NigamKMccallumAKThrunSText classification from labeled and unlabeled documents using EMMach Learn20003910313410.1023/A:10076927130850949.68162 – reference: WallachHMimnoDMcCallumABengioYSchuurmansDLaffertyJRethinking LDA: why priors matterAdvances in neural information processing systems2009New YorkCurran Associates Inc. – reference: Feinerer I, Hornik K (2020) tm: text mining package. https://CRAN.R-project.org/package=tm, R package version 0.7-8 – reference: GhahramaniZProbabilistic machine learning and artificial intelligenceNature201552145245910.1038/nature14541 – reference: CeleuxGFrüwirth-SchnatterSRobertCPFrühwirth-SchnatterSCeleuxGRobertCPModel selection for mixture models—perspectives and strategiesHandbook of mixture analysis2018New YorkChapmann & Hall11815410.1201/9780429055911 – reference: HornikKFeinererIKoberMSpherical k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document}-means clusteringJ Stat Softw201210.18637/jss.v050.i10 – reference: BleiDMKucukelbirAMcAuliffeJDVariational inference: a review for statisticiansJ Am Stat Assoc2017112859877367177610.1080/01621459.2017.1285773 – reference: Andrews N, Fox E (2007) Recent developments in document clustering. http://hdl.handle.net/10919/19473, Virginia Tech computer science technical report, TR-07-35 – reference: AnderlucciLViroliCMixtures of Dirichlet-multinomial distributions for supervised and unsupervised classification of short text dataAdv Data Anal Classif202014759770420157210.1007/s11634-020-00399-31459.62102 – reference: LiHFanXA pivotal allocation-based algorithm for solving the label-switching problem in Bayesian mixture modelsJ Comput Graph Stat201625266283347404710.1080/10618600.2014.983643 – reference: Malsiner-WalliGFrühwirth-SchnatterSGrünBModel-based clustering based on sparse finite Gaussian mixturesStat Comput201626303324343937510.1007/s11222-014-9500-21342.62109 – reference: MosimannJEOn the compound multinomial distribution, the multivariate Beta-distribution, and correlations among proportionsBiometrika196249658214329910.1093/biomet/49.1-2.650105.12502 – reference: ManningCDRaghavanPSchützeHIntroduction to information retrieval2008CambridgeCambridge University Press10.1017/CBO97805118090711160.68008 – reference: Tran MN, Nguyen TN, Dao VH (2021) A practical tutorial on variational Bayes. arXiv:2103.01327 – reference: PolliceABilanciaMA hierarchical finite mixture model for Bayesian classification in the presence of auxiliary informationMetron Int J Stat2000LVIII10913118542250999.62504 – reference: Greene D, Cunningham P (2006) Practical solutions to the problem of diagonal dominance in kernel document clustering. In: Proceedings of the 23rd international conference on machine learning (ICML’06). ACM Press, pp 377–384 – reference: BleiDMNgAYJordanMILatent Dirichlet allocationJ Mach Learn Res2003399310221112.68379 – reference: Frühwirth-SchnatterSEstimating marginal likelihoods for mixture and Markov switching models using bridge sampling techniquesEconom J20047143167207663010.1111/j.1368-423X.2004.00125.x1053.62087 – reference: MurphyKPMachine learning: a probabilistic perspective2012CambridgeThe MIT Press1295.68003 – year: 2007 ident: 1350_CR12 publication-title: Ann Appl Stat doi: 10.1214/07-AOAS114 – ident: 1350_CR62 doi: 10.1007/978-3-319-16181-5_39 – start-page: 73 volume-title: Handbook of mixture analysis year: 2018 ident: 1350_CR17 doi: 10.1201/9780429055911 – volume: 62 start-page: 49 year: 2000 ident: 1350_CR34 publication-title: Sankhyā Indian J Stat Ser A (1961–2002) – volume: 26 start-page: 303 year: 2016 ident: 1350_CR39 publication-title: Stat Comput doi: 10.1007/s11222-014-9500-2 – volume: 62 start-page: 795 year: 2000 ident: 1350_CR54 publication-title: J R Stat Soc Ser B (Stat Methodol) doi: 10.1111/1467-9868.00265 – start-page: 305 volume-title: Data clustering: algorithms and applications year: 2014 ident: 1350_CR3 – volume: 22 start-page: 455 year: 2012 ident: 1350_CR9 publication-title: Stat Comput doi: 10.1007/s11222-011-9236-1 – volume: 5 start-page: 2 year: 2008 ident: 1350_CR41 publication-title: Indian Bayesian Soc Newslett – volume: 83 start-page: 173 year: 1988 ident: 1350_CR19 publication-title: J Am Stat Assoc doi: 10.2307/2288938 – year: 2012 ident: 1350_CR31 publication-title: J Stat Softw doi: 10.18637/jss.v050.i10 – volume-title: Machine learning: a probabilistic perspective year: 2012 ident: 1350_CR43 – volume: LVIII start-page: 109 year: 2000 ident: 1350_CR48 publication-title: Metron Int J Stat – volume: 12 start-page: 233 year: 1994 ident: 1350_CR6 publication-title: ACM Trans Inf Syst doi: 10.1145/183422.183423 – volume: 112 start-page: 859 year: 2017 ident: 1350_CR14 publication-title: J Am Stat Assoc doi: 10.1080/01621459.2017.1285773 – volume: 10 start-page: 146 year: 1954 ident: 1350_CR29 publication-title: WORD doi: 10.1080/00437956.1954.11659520 – ident: 1350_CR38 – volume: 521 start-page: 452 year: 2015 ident: 1350_CR27 publication-title: Nature doi: 10.1038/nature14541 – volume-title: Mining text data year: 2012 ident: 1350_CR1 doi: 10.1007/978-1-4614-3223-4 – volume: 41 start-page: 2311 year: 2021 ident: 1350_CR10 publication-title: IMA J Numer Anal doi: 10.1093/imanum/draa038 – volume: 55 start-page: 77 year: 2012 ident: 1350_CR11 publication-title: Commun ACM doi: 10.1145/2133806.2133826 – volume-title: Introduction to information retrieval year: 2008 ident: 1350_CR40 doi: 10.1017/CBO9780511809071 – volume: 49 start-page: 65 year: 1962 ident: 1350_CR42 publication-title: Biometrika doi: 10.1093/biomet/49.1-2.65 – volume: 1 start-page: 1 year: 2007 ident: 1350_CR58 publication-title: Found Trends® Mach Learn doi: 10.1561/2200000001 – year: 2006 ident: 1350_CR55 publication-title: Bayesian Anal doi: 10.1214/06-BA121 – volume: 41 start-page: 2008 year: 2019 ident: 1350_CR61 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2018.2889774 – ident: 1350_CR28 doi: 10.1145/1143844.1143892 – volume: 37 start-page: 183 year: 1999 ident: 1350_CR32 publication-title: Mach Learn doi: 10.1023/A:1007665907178 – volume: 3 start-page: 993 year: 2003 ident: 1350_CR13 publication-title: J Mach Learn Res – volume: 42 start-page: 143 year: 2001 ident: 1350_CR20 publication-title: Mach Learn doi: 10.1023/A:1007612920971 – ident: 1350_CR23 – ident: 1350_CR44 – year: 2021 ident: 1350_CR36 publication-title: Commun Stat Theory Methods doi: 10.1080/03610926.2021.1921214 – start-page: 105 volume-title: Natural language processing and information systems year: 2020 ident: 1350_CR50 doi: 10.1007/978-3-030-51310-8_10 – volume: 39 start-page: 103 year: 2000 ident: 1350_CR45 publication-title: Mach Learn doi: 10.1023/A:1007692713085 – ident: 1350_CR56 – volume: 14 start-page: 759 year: 2020 ident: 1350_CR4 publication-title: Adv Data Anal Classif doi: 10.1007/s11634-020-00399-3 – start-page: 118 volume-title: Handbook of mixture analysis year: 2018 ident: 1350_CR16 doi: 10.1201/9780429055911 – volume: 20 start-page: 599 year: 2019 ident: 1350_CR52 publication-title: Biostatistics doi: 10.1093/biostatistics/kxy018 – volume: 56 start-page: 363 year: 1994 ident: 1350_CR21 publication-title: J R Stat Soc Ser B (Methodol) doi: 10.1111/j.2517-6161.1994.tb01985.x – ident: 1350_CR33 – volume-title: The elements of statistical learning year: 2009 ident: 1350_CR30 doi: 10.1007/978-0-387-84858-7 – volume-title: Finite mixture and Markov switching models year: 2006 ident: 1350_CR25 doi: 10.1007/978-0-387-35768-3 – volume: 7 start-page: 143 year: 2004 ident: 1350_CR24 publication-title: Econom J doi: 10.1111/j.1368-423X.2004.00125.x – volume-title: The Bayesian choice year: 2007 ident: 1350_CR51 doi: 10.1007/0-387-71599-1 – volume: 22 start-page: 1263 year: 2020 ident: 1350_CR47 publication-title: Entropy doi: 10.3390/e22111263 – ident: 1350_CR53 – volume: 95 start-page: 957 year: 2000 ident: 1350_CR15 publication-title: J Am Stat Assoc doi: 10.1080/01621459.2000.10474285 – year: 2008 ident: 1350_CR22 publication-title: J Stati Softw doi: 10.18637/jss.v025.i05 – volume-title: Advances in neural information processing systems year: 2015 ident: 1350_CR7 – volume: 9 start-page: 2579 year: 2008 ident: 1350_CR57 publication-title: J Mach Learn Res – volume: 25 start-page: 713 year: 2015 ident: 1350_CR8 publication-title: Stat Comput doi: 10.1007/s11222-015-9561-x – volume: 2 start-page: 165 year: 2015 ident: 1350_CR60 publication-title: Ann Data Sci doi: 10.1007/s40745-015-0040-1 – volume-title: Handbook of mixed membership models and their applications year: 2014 ident: 1350_CR2 doi: 10.1201/b17520 – ident: 1350_CR49 – volume-title: Advances in neural information processing systems year: 2009 ident: 1350_CR59 – volume: 25 start-page: 266 year: 2016 ident: 1350_CR37 publication-title: J Comput Graph Stat doi: 10.1080/10618600.2014.983643 – ident: 1350_CR46 – ident: 1350_CR18 – volume-title: Bayesian data analysis year: 2013 ident: 1350_CR26 doi: 10.1201/b16018 – year: 2020 ident: 1350_CR35 publication-title: Electron J Stat doi: 10.1214/20-EJS1756 – ident: 1350_CR5 |
| SSID | ssj0022721 |
| Score | 2.3582969 |
| Snippet | In this paper, we formulate a hierarchical Bayesian version of the Mixture of Unigrams model for text clustering and approach its posterior inference through... |
| SourceID | unpaywall proquest crossref springer |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 2015 |
| SubjectTerms | Algorithms Approximation Clustering Dirichlet problem Economic Theory/Quantitative Economics/Mathematical Methods Inference Iterative methods Markov analysis Markov chains Mathematics and Statistics Maxima Mixtures Monte Carlo simulation Original Paper Probability and Statistics in Computer Science Probability distribution Probability Theory and Stochastic Processes Statistics |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LSwNBDA61HrQH8Yn1xRy86eA-Zl8HERWlCBYRFW_LvIqF2lbbov33JtPdrV6K133MLsnM5Msk-QJwLK1vI20FV74RXBjtcSk7CQ90rMl7RszgEmTbcetZ3L1GrzVol7UwlFZZ7oluozYDTWfkZ0GaZglxWPoXww9OXaMoulq20JBFawVz7ijGlmA5IGasOixf3bQfHisXLEhcJRal06HnFAdFGY0rpqP-dB5HG4budRh5PP1rqub4swqZNmBl0h_K6Zfs9X5Zpdt1WCvgJLuc6X8Dara_CY37iot1tAX5C7rDxZEfu5JTO2LErDErWWSDDqNu2C6egOpiuAV29Rsqk7tUQypaxqvv3W-KNIwYQlxGuSJM9yZEsYA_uA3PtzdP1y1etFXgOozDMRdUJJVZEWmUpghVIIxCUGeVtLGNUmWyjhDKyNCkUWSVDkQkEAahGUuECXQa7kC9P-jbXWBJgvhGWE9L4eGwnYyWf5aiVVS-QqjRBL-UYK4LznFqfdHLK7ZkJ_UcpZ47qedpE06qd4Yzxo2FTx-UismL1TfK53OlCaelsua3F412Win0Hx_fW_zxfVil5vSz5JcDqI8_J_YQIcxYHRXz8geOR-pv priority: 102 providerName: ProQuest – databaseName: SpringerLink Journals (ICM) dbid: U2A link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB60HmwPolWxWmUP3uxCHrt5HKtYiqAnK72F3c0GCzEtpkX7753NqwpS9JrHJOTb3fkmO_MNwLXQtuZKMyrtmFEWK4sKkfjUUZ4y0TNyhiJB9skbT9jDlE-rorC8znavtySLlbopdjP94yyKPgbDX5dbNNiFPW7kvHAUT5xhE2Y5flFtZVLmMDrynKpU5ncbP93RhmM226Id2F9lC7H-EGn6zfOMDuGgooxkWGJ8BDs660LnsdFbzbvQNpyxlFw-hugF49_qHx-5FWudEyOlUdYoknlCTPvrYgMB8SG45s3UK6JHi9xCU6WMR99mn2ZrISfIaYlJDiEqXRlNBXzbE5iM7p_vxrTqo0CV67lLykxVVKgZV0j3mCsdFktkcVoK7WkeyDhMGJOxcOOAcy2VwzhD3oN-y2exowL3FFrZPNNnQHwfCQ3TlhLMQrNJaOZ7GKAblLZEbtEDu_6ckapExk2vizRq5JELCCKEICogiIIe3DT3LEqJja1X92uUomq65ZETBKFvBE7tHgxq5Dant1kbNOj-4eHn_7N-AW3Tnb7MfulDa_m-0pfIYZbyqhiyX_sF5OE priority: 102 providerName: Springer Nature |
| Title | Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering |
| URI | https://link.springer.com/article/10.1007/s00180-023-01350-8 https://www.proquest.com/docview/2889791391 https://hdl.handle.net/11586/429445 |
| UnpaywallVersion | submittedVersion |
| Volume | 38 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Mathematics Source - trial do 30.11.2025 customDbUrl: eissn: 1613-9658 dateEnd: 20241101 omitProxy: false ssIdentifier: ssj0022721 issn: 0943-4062 databaseCode: AMVHM dateStart: 20110301 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/mathematics-source providerName: EBSCOhost – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1613-9658 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0022721 issn: 0943-4062 databaseCode: AFBBN dateStart: 19990301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1613-9658 dateEnd: 20241101 omitProxy: true ssIdentifier: ssj0022721 issn: 0943-4062 databaseCode: BENPR dateStart: 19990301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1613-9658 dateEnd: 20241101 omitProxy: true ssIdentifier: ssj0022721 issn: 0943-4062 databaseCode: 8FG dateStart: 19990301 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1613-9658 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0022721 issn: 0943-4062 databaseCode: AGYKE dateStart: 19990101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1613-9658 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0022721 issn: 0943-4062 databaseCode: U2A dateStart: 20040212 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dT9swED9B-4B44GNsWqeussTehiEf58R5LKylArVCE0XwFNmOqyFKqWirAX895zQJjAe2vcRS7Dixzvb9Lr77HcA3ZX0rjEWu_Qw5ZsbjSo1iHpjIOOuZMEPuIDuIekM8uRSXK7BbxsK84RcgvCKjA9ozEcUq1CNBgLsG9eHgrH2Vs-hhSBZQnjaUoEvIHZVJERqTB8i5nHMeJ71EJnMoPC7_VD8vmLI6Bl2HtcVkqh5_q_H4labpbsKP8huXDiY3-4u53jdPb-gb_zKILdgokCZrL6fGNqzYyQdY71c0rbMdSC_IUi7-BrJD9WhnzJFuLKMZ2d2IuUTZ-VEDSZLR7nhtfpGcee6F6OKZ6e7t9YM7hJgxQr_MuZEwM1449gUa50cYdjvnRz1eZFzgJozCOUcXP5VYFIaAIYY6wEwT3rNa2cgKqbNkhKgzFWZSCKtNgAIJIZGGizELjAw_QW1yN7GfgcUxQR-0nlHoUbejxO0MiSSFqX1NKKQBfimI1BR05C4rxjitiJRz4aUkvDQXXiob8L16Zrok43i3dbOUb1oszFkaSJnEjgrVb8BeKfOX6vd626vmxT-8_Mv_NW9CbX6_sF8J4Mx1C1blke-u3eMW1NvHV6cdV_Yven0qDzuDs59UOwzarWIpPAMzePm0 |
| linkProvider | Unpaywall |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxEB6V9lB6QDxFoIAPcKIWu17v61AhCq1S2kYItag341dEpZAENlHJn-O3MeN4N3CJuPS6j9nVzHj8jecF8FL71OfWS25SJ7l0NuFaD0subGHJe0bMEBJkB0X_Qn68zC834HdbC0Npla1NDIbaTSydkb8RVVWX1MMyfTv9wWlqFEVX2xEaOo5WcPuhxVgs7Djxi2t04Zr94w8o71dCHB2ev-_zOGWA26zIZlxSzVDtZW6RuMyMkM4gxvFG-8LnlXH1UErjdOaqPPfGCplLRAVo1UvphK0ypHsLtmQma3T-tg4OB58-dy6fKEPlF6XvoadWiFi2E4r3aB5ewnHPRHc-yxNe_bs1rvBuF6Ldge35eKoX13o0-msXPLoLdyJ8Ze-W-nYPNvz4Puycdb1fmwegvqD7HY8Y2YFe-IZRJ49liSSbDBlN3w7xC1QPhib3yn5D5eEhtZGKpPHq96tfFNloGEJqRrkpzI7m1NIBf_AhXNwIgx_B5ngy9o-BlSXiKekTq2WCZIc1mZu6wl3YpAahTQ_SloPKxh7nNGpjpLruzIHrCrmuAtdV1YPX3TvTZYePtU_vtoJRcbU3aqWbPdhrhbW6vY7aXifQ__j4k_UffwHb_fOzU3V6PDh5CrcFqVZIvNmFzdnPuX-G8GlmnkcdZfD1ppfFH4wRJo4 |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VIgE9VLwqlrbgA5yo1cRxEueAqj5YtRQqDhT1ZmzHEZWW3W2zq7J_rb-OGW-SLZcVl17zmEQz4_E3nhfAO-NjnzovuY1LyWXpIm5MlXPhMkfeM2KGkCB7lh2fy88X6cUK3La1MJRW2drEYKjLkaMz8l2hVJFTD8t4t2rSIr4d9ffGV5wmSFGktR2nMVeRUz-7Qfet_nhyhLJ-L0T_0_fDY95MGOAuyZIJl1QvVHiZOiQsEytkaRHfeGt85lNly6KS0pYmKVWaeuuETCUiArTouSyFUwnSfQAP8yTLqG-_OuzSS4TIQ80XJe6hj5aJpmAnlO3RJLyI426JjnySRlz9uykukG4XnF2Dx9Ph2MxuzGBwZ__rP4X1Briy_bmmPYMVP3wOa1-7rq_1C9A_0PFuDhfZgZn5mlEPj3lxJBtVjOZuh8gFKgZDY3vpfqHa8JDUSOXRePX35R-KadQMwTQj9jM3mFIzB_zBl3B-L-zdgNXhaOhfActzRFLSR87ICMlWBRmaQuH-a2OLoKYHcctB7Zru5jRkY6C7vsyB6xq5rgPXterBh-6d8by3x9Knt1rB6Gad13qhlT3YaYW1uL2M2k4n0P_4-OvlH38Lj3Ax6C8nZ6eb8ESQZoWMmy1YnVxP_Tbipol9ExSUwc_7XhF_AQPLI_M |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3JTsMwEB1Be0A9sCOKAFmCGxiyjBPnyCqEBOJAUTlFtuOKilIQbcXy9YzTJCyHAtfEcWI9x_PGnnkDsK2sb4WxyLWfIcfMeFypTswDExnnPRNnyANkL6OzFp63RXsKtspcmB_6AsRXZLRPayaimIZ6JIhw16Deurw6uM1V9DAkDygvG0rUJeROyqRIjckT5FzNOY-TXSKXORQel9_NzyenrI5BGzAz6j-ptxfV632xNKdzcFx-4zjA5H5vNNR75v2HfOMvg5iH2YJpsoPx1FiAKdtfhMZFJdM6WIL0hjzlYjeQHao3O2BOdGOczcgeO8wVys6PGghJRqtj19wRzjyPQnT5zHT1ofvqDiEGjNgvc2EkzPRGTn2BxrkMrdOT66MzXlRc4CaMwiFHlz-VWBSGiCGGOsBME9-zWtnICqmzpIOoMxVmUgirTYACiSGRhYsxC4wMV6DWf-zbVWBxTNQHrWcUetRtJ3ErQyLJYGpfEwtpgl8CkZpCjtxVxeillZByDl5K4KU5eKlswk71zNNYjGNi6_US37T4MQdpIGUSOylUvwm7Jeaftyf1tlvNiz-8fO1_zdehNnwe2Q0iOEO9WUzwD-qQ8k4 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Variational+Bayes+estimation+of+hierarchical+Dirichlet-multinomial+mixtures+for+text+clustering&rft.jtitle=Computational+statistics&rft.au=Bilancia%2C+Massimo&rft.au=Di+Nanni%2C+Michele&rft.au=Manca%2C+Fabio&rft.au=Pio%2C+Gianvito&rft.date=2023-12-01&rft.issn=0943-4062&rft.eissn=1613-9658&rft.volume=38&rft.issue=4&rft.spage=2015&rft.epage=2051&rft_id=info:doi/10.1007%2Fs00180-023-01350-8&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s00180_023_01350_8 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0943-4062&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0943-4062&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0943-4062&client=summon |