Open AccessSparse Weighted Canonical Correlation Analysis
Author(s) -
MIN Wenwen,
LIU Juan,
ZHANG Shihua
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.08.004
Subject(s) - canonical correlation , lasso (programming language) , mathematics , correlation , algorithm , computer science , pattern recognition (psychology) , artificial intelligence , statistics , geometry , world wide web
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.