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

文献类型：期刊论文

作者Zhang, Lihua1,2; Zhang, Shihua1,2
刊名IEEE TRANSACTIONS ON FUZZY SYSTEMS
出版日期2020-09-01
卷号28期号:9页码:1971-1983
ISSN号1063-6706
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
DOI10.1109/TFUZZ.2019.2928518
英文摘要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.
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
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000566682000007
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/52175]  
专题应用数学研究所
通讯作者Zhang, Shihua
作者单位1.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
2.Chinese Acad Sci, NCMIS, CEMS, RCSDS,Acad Math & Syst Sci, Beijing 100190, 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.

入库方式： OAI收割

来源：数学与系统科学研究院

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