KMS Of Academy of mathematics and systems sciences, CAS
| Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise | |
Zhang, Chihao1,2,3; Zhang, Shihua1,2,3
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| 2021-04-01 | |
| Source Publication | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| ISSN | 0162-8828 |
| Volume | 43Issue:4Pages:1184-1196 |
| Abstract | Matrix decomposition is a popular and fundamental approach in machine learning and data mining. It has been successfully applied into various fields. Most matrix decomposition methods focus on decomposing a data matrix from one single source. However, it is common that data are from different sources with heterogeneous noise. A few of the matrix decomposition methods have been extended for such multi-view data integration and pattern discovery while only a few methods were designed to consider the heterogeneity of noise in such multi-view data for data integration explicitly. To this end, in this article, we propose a joint matrix decomposition framework (BJMD), which models the heterogeneity of noise by the Gaussian distribution in a Bayesian framework. We develop two algorithms to solve this model: one is a variational Bayesian inference algorithm, which makes full use of the posterior distribution; and another is a maximum a posterior algorithm, which is more scalable and can be easily paralleled. Extensive experiments on synthetic and real-world datasets demonstrate that BJMD is superior or competitive to the state-of-the-art methods. |
| Keyword | Matrix decomposition Bayes methods Data integration Inference algorithms Data models Data mining Gaussian distribution Bayesian methods matrix decomposition data integration variational Bayesian inference maximum a posterior |
| DOI | 10.1109/TPAMI.2019.2946370 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; National Ten Thousand Talent Program for Young Top-notch Talents ; Key Research Program of the Chinese Academy of Sciences[KFZD-SW-219] ; National Key Research and Development Program of China[2017YFC0908405] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008] |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000626525300006 |
| Publisher | IEEE COMPUTER SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/58364 |
| Collection | 应用数学研究所 |
| Corresponding Author | Zhang, Shihua |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China |
| Recommended Citation GB/T 7714 | Zhang, Chihao,Zhang, Shihua. Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(4):1184-1196. |
| APA | Zhang, Chihao,&Zhang, Shihua.(2021).Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(4),1184-1196. |
| MLA | Zhang, Chihao,et al."Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.4(2021):1184-1196. |
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