MIN Wenwen, LIU Juan, ZHANG Shihua. Sparse Weighted Canonical Correlation Analysis[J]. Chinese Journal of Electronics, 2018, 27(3): 459-466. doi: 10.1049/cje.2017.08.004
Citation: MIN Wenwen, LIU Juan, ZHANG Shihua. Sparse Weighted Canonical Correlation Analysis[J]. Chinese Journal of Electronics, 2018, 27(3): 459-466. doi: 10.1049/cje.2017.08.004

Sparse Weighted Canonical Correlation Analysis

doi: 10.1049/cje.2017.08.004
Funds:  This work is supported by the National Natural Science Foundation of China (No.61422309, No.61379092, No.61621003, No.11661141019), the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (No.XDB13040600), the National Science Foundation of Jiangsu Province (No.BK20161249), the Fundamental Research Funds for the Central Universities (No.2042017KF0233), CAS Frontier Science Research Key Project for Top Young Scientist (No.QYZDBSSW-SYS008), and the Key Laboratory of Random Complex Structures and Data Science, CAS (No.2008DP173182).
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  • Corresponding author: LIU Juan (corresponding author) was born in 1970. She received the Ph.D. degree in computer science from Wuhan University and now serves as a professor and Ph.D. supervisor in Wuhan University. Her research interests include data mining, nature language process and bioinformatics. (Email:liujuan@whu.edu.cn)
  • Received Date: 2016-12-02
  • Rev Recd Date: 2017-06-06
  • Publish Date: 2018-05-10
  • 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 L0-regularized SWCCA (L0-SWCCA) using an alternating iterative algorithm. We apply L0-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.
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