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

The Journal of Engineering

Chuan-cai Liu¹  Zhong-rong Shi²  Sheng Wang³ 

[1] Institute of Image Processing and Pattern Recognition, Henan University, Kaifeng 475004, People'School of Computer and Engineering, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China

关键词: group sparse feature selection;    distance measurement;    robust;    sparse CCA;    RSCCA-based L2;    p-norm;    canonical correlation analysis;    objective function;    L2-norm distance minimization;    feature fusion method;    robust feature extraction;    paired data;   

DOI  :  10.1049/joe.2016.0296

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

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.

【 授权许可】

CC BY