Robust multi-view discriminant analysis with view-consistency

Abstract

Multi-view discriminant analysis (MvDA) is a successful method to learn a single discriminant common space of multiple views. However, MvDA may encounter the robustness issue theoretically because of using F-norm as the metric. In this paper, a robust multi-view discriminant analysis with view-consistency is proposed by employing L1-norm as the metric, called as L1-MvDA-VC. where both inter-view and intra-view distances are characterized by L1-norm. The proposed L1-MvDA-VC not only can obtain discriminant common space, but also is robust to outliers. In addition, a simple and effective learning algorithm is designed for L1-MvDA-VC, and its convergence is proved theoretically. Experimental results on real data sets demonstrate that L1-MvDA-VC has better performance on contaminated data than that of MvDA, MvDA-VC, MvCCDA, and MULDA.