Work

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

Year: 2017

Type: article

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.

Source: The Journal of Engineering

DOI: 10.1049/joe.2016.0296

Authors: Zhong‐rong Shi, Sheng Wang, Chuan‐cai Liu

Institutions: Nanjing University of Science and Technology, Henan University

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,000

Funder: National Natural Science Foundation of China