Robust and sparse canonical correlation analysis based L(2,p)-norm

Abstract

The objective function of canonical correlation analysis (CCA) is equivalent to minimising an L(2)-norm distance of the paired data. Owing to the characteristic of L(2)-norm, CCA is highly sensitive to noise and irrelevant features. To alleviate such problem, this study incorporates robust feature extraction and group sparse feature selection into the framework of CCA, and proposes a feature fusion method named robust and sparse CCA (RSCCA). In RSCCA, L(2,p)-norm is adopted as the distance measurement of paired data, which can alleviate the effect of noise and irrelevant features and achieve robust performance. The experimental results show that our method outperforms CCA and its variants for feature fusion.

Keywords

• feature selection
• feature extraction
• paired data
• distance measurement
• robust and sparse CCA
• feature fusion method
• group sparse feature selection
• robust feature extraction
• L(2)-norm distance minimization
• canonical correlation analysis
• objective function
• RSCCA-based L(2,p)-norm