Practical applications of sparse modeling
Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling,...
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| Other Authors | , , , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Cambridge, Massachusetts :
The MIT Press,
[2014]
|
| Series | Neural information processing series.
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9780262325325 9780262027724 |
| Physical Description | 1 online zdroj (xii, 249 pages) |
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| 245 | 0 | 0 | |a Practical applications of sparse modeling / |c edited by Irina Rish, Guillermo A. Cecchi, Aurelie Lozano, and Alexandru Niculescu-Mizil. |
| 264 | 1 | |a Cambridge, Massachusetts : |b The MIT Press, |c [2014] | |
| 300 | |a 1 online zdroj (xii, 249 pages) | ||
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| 490 | 1 | |a Neural information processing series | |
| 520 | 8 | |a Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models.ContributorsA. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, Rémi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing. | |
| 521 | |a Scholarly & Professional |b MIT Press | ||
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a IEEE |b MIT Press eBooks Library-Computing & Engineering Collection Complete | ||
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty univerzity | ||
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Sampling (Statistics) | |
| 650 | 0 | |a Data reduction. | |
| 650 | 0 | |a Sparse matrices. | |
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| 700 | 1 | |a Rish, Irina, |d 1969- |e editor. | |
| 700 | 1 | |a Cecchi, Guillermo A., |e editor. | |
| 700 | 1 | |a Lozano, Aurélie Chloé, |d 1975- |e editor. | |
| 700 | 1 | |a Niculescu-Mizil, Alexandru, |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Practical applications of sparse modeling. |d Cambridge, Massachusetts : The MIT Press, [2014] |z 9780262027724 |w (DLC) 2014003812 |w (OCoLC)875055773 |
| 830 | 0 | |a Neural information processing series. | |
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