A unified robust framework for multi-view feature extraction with L2,1-norm constraint.

Abstract

Multi-view feature extraction methods mainly focus on exploiting the consistency and complementary information between multi-view samples, and most of the current methods apply the F-norm or L2-norm as the metric, which are sensitive to the outliers or noises. In this paper, based on L2,1-norm, we propose a unified robust feature extraction framework, which includes four special multi-view feature extraction methods, and extends the state-of-art methods to a more generalized form. The proposed methods are less sensitive to outliers or noises. An efficient iterative algorithm is designed to solve L2,1-norm based methods. Comprehensive analyses, such as convergence analysis, rotational invariance analysis and relationship between our methods and previous F-norm based methods illustrate the effectiveness of our proposed methods. Experiments on two artificial datasets and six real datasets demonstrate that the proposed L2,1-norm based methods have better performance than the related methods.