Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
文献类型：期刊论文
| 作者 | Zhang, Chihao1,2,3; Zhang, Shihua1,2,3
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
 |
| 出版日期 | 2021-04-01 |
| 卷号 | 43期号:4页码:1184-1196 |
| 关键词 | 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 |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2019.2946370 |
| 英文摘要 | 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. |
| 资助项目 | 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研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000626525300006 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/58363]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhang, Shihua |
| 作者单位 | 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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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