CSpace  > 应用数学研究所
Min Wenwen1; Liu Juan1; Zhang Shihua2
Source Publicationchinesejournalofelectronics
AbstractGiven 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.
Funding Project[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]
Document Type期刊论文
Recommended Citation
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.
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