Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise

doi:10.1109/TPAMI.2019.2946370

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	Bayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
	Zhang, Chihao 1,2,3; Zhang, Shihua 1,2,3
刊名	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	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
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000626525300006
内容类型	期刊论文
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.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, Beijing 100190, 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.