sparseweightedcanonicalcorrelationanalysis

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	sparseweightedcanonicalcorrelationanalysis
	Min Wenwen 1; Liu Juan 1; Zhang Shihua2
	2018
发表期刊	chinesejournalofelectronics
ISSN	1022-4653
卷号	27 期号:3 页码:459
摘要	Given two data matrices X and Y, Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Yv. Classical and sparse Canonical correlation analysis (CCA) models consider the contribution of all the samples of data matrices and thus cannot identify an underlying specific subset of samples. We propose a novel Sparse weighted canonical correlation analysis (SWCCA), where weights are used for regularizing different samples. We solve the L-0-regularized SWCCA (L-0-SWCCA) using an alternating iterative algorithm. We apply L-0-SWCCA to synthetic data and real-world data to demonstrate its effectiveness and superiority compared to related methods. We consider also SWCCA with different penalties like Least absolute shrinkage and selection operator (LASSO) and Group LASSO, and extend it for integrating more than three data matrices.
语种	英语
资助项目	[National Natural Science Foundation of China] ; [Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)] ; [National Science Foundation of Jiangsu Province] ; [Fundamental Research Funds for the Central Universities] ; [CAS Frontier Science Research Key Project for Top Young Scientist] ; [Key Laboratory of Random Complex Structures and Data Science, CAS]
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/46013
专题	应用数学研究所
作者单位	1.武汉大学 2.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Min Wenwen,Liu Juan,Zhang Shihua. sparseweightedcanonicalcorrelationanalysis[J]. chinesejournalofelectronics,2018,27(3):459.
APA	Min Wenwen,Liu Juan,&Zhang Shihua.(2018).sparseweightedcanonicalcorrelationanalysis.chinesejournalofelectronics,27(3),459.
MLA	Min Wenwen,et al."sparseweightedcanonicalcorrelationanalysis".chinesejournalofelectronics 27.3(2018):459.