A Multi-Kernel-Based Multi-View Deep Non-Negative Matrix Factorization for Enhanced Healthcare Data Clustering
Multi-view clustering methods based on deep matrix factorization play a vital role in data analysis within the healthcare sector. However, existing methods predominantly conduct deep matrix factorization in the original data space, which is not conducive to addressing non-linear and complex data pat...
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| Published in | IEEE transactions on consumer electronics Vol. 71; no. 1; pp. 1442 - 1452 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0098-3063 1558-4127 |
| DOI | 10.1109/TCE.2024.3440485 |
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| Abstract | Multi-view clustering methods based on deep matrix factorization play a vital role in data analysis within the healthcare sector. However, existing methods predominantly conduct deep matrix factorization in the original data space, which is not conducive to addressing non-linear and complex data patterns. To address this issue, the Multi-kernel based Multi-view Deep Non-negative Matrix Factorization with Optimal Consensus Graph (OGMKMDNMF) is introduced. This approach utilizes deep non-negative matrix factorization after projecting the data matrix into a high-dimensional kernel space. Additionally, it employs optimal consensus graph to alleviate the detrimental effects arising from misassigned nearest neighbors during the construction of similarity matrix. An innovative iterative optimization algorithm is developed for OGMKMDNMF. The experimental results demonstrate the effectiveness and competitive advantage of OGMKMDNMF in addressing multi-view healthcare data clustering tasks. |
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| AbstractList | Multi-view clustering methods based on deep matrix factorization play a vital role in data analysis within the healthcare sector. However, existing methods predominantly conduct deep matrix factorization in the original data space, which is not conducive to addressing non-linear and complex data patterns. To address this issue, the Multi-kernel based Multi-view Deep Non-negative Matrix Factorization with Optimal Consensus Graph (OGMKMDNMF) is introduced. This approach utilizes deep non-negative matrix factorization after projecting the data matrix into a high-dimensional kernel space. Additionally, it employs optimal consensus graph to alleviate the detrimental effects arising from misassigned nearest neighbors during the construction of similarity matrix. An innovative iterative optimization algorithm is developed for OGMKMDNMF. The experimental results demonstrate the effectiveness and competitive advantage of OGMKMDNMF in addressing multi-view healthcare data clustering tasks. |
| Author | Che, Hangjun Yang, Xuanhao |
| Author_xml | – sequence: 1 givenname: Hangjun orcidid: 0000-0002-8930-0039 surname: Che fullname: Che, Hangjun email: hjche123@swu.edu.cn organization: College of Electronic and Information Engineering and the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China – sequence: 2 givenname: Xuanhao surname: Yang fullname: Yang, Xuanhao email: xhyang0121@foxmail.com organization: College of Electronic and Information Engineering, Southwest University, Chongqing, China |
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| SubjectTerms | Clustering Clustering algorithms Consumer electronics Data analysis Data models deep matrix factorization Factorization Health care Healthcare information fusion Matrix decomposition Medical information systems Medical services multi-kernel learning multi-view clustering Optimization Task analysis |
| Title | A Multi-Kernel-Based Multi-View Deep Non-Negative Matrix Factorization for Enhanced Healthcare Data Clustering |
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