A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration

doi:10.1109/TFUZZ.2019.2928518

CSpace > 应用数学研究所

	A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration
	Zhang, Lihua 1,2; Zhang, Shihua1,2
	2020-09-01
发表期刊	IEEE TRANSACTIONS ON FUZZY SYSTEMS
ISSN	1063-6706
卷号	28 期号:9 页码:1971-1983
摘要	Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory analysis by discovering hidden features and part-based patterns from high-dimensional data. NMF and its variants have been successfully applied into diverse fields such as pattern recognition, signal processing, data mining, bioinformatics, and so on. Recently, NMF has been extended to analyze multiple matrices simultaneously. However, a general framework and its systematic algorithmic exploration are still lacking. In this paper, we first introduce a sparse multiple relationship data regularized joint matrix factorization (JMF) framework and two adapted prediction models for pattern recognition and data integration. Next, we present four update algorithms to solve this framework in a very comprehensive manner. The merits and demerits of these algorithms are systematically explored. Furthermore, extensive computational experiments using both synthetic data and real data demonstrate the effectiveness of JMF framework and related algorithms on pattern recognition and data mining.
关键词	Sparse matrices Pattern recognition Data integration Prediction algorithms Data models Matrix decomposition Signal processing algorithms Bioinformatics data integration network-regularized constraint nonnegative matrix factorization (NMF) pattern recognition
DOI	10.1109/TFUZZ.2019.2928518
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[61422309] ; National Natural Science Foundation of China[61379092] ; Strategic Priority Research Program of the Chinese Academy of Sciences[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] ; CASFrontier Science ResearchKey Project for TopYoung Scientist[QYZDB-SSW-SYS008]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000566682000007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/52176
专题	应用数学研究所
通讯作者	Zhang, Shihua
作者单位	1.Chinese Acad Sci, NCMIS, CEMS, RCSDS,Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Lihua,Zhang, Shihua. A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS,2020,28(9):1971-1983.
APA	Zhang, Lihua,&Zhang, Shihua.(2020).A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration.IEEE TRANSACTIONS ON FUZZY SYSTEMS,28(9),1971-1983.
MLA	Zhang, Lihua,et al."A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration".IEEE TRANSACTIONS ON FUZZY SYSTEMS 28.9(2020):1971-1983.