Consistent Discriminant Correlation Analysis

Abstract

Multi-view dimensionality reduction is an importan subject in multi-view learning. Canonical correlation analysis and its various improved forms can effectively solve this problem. But most of these algorithms do not fully consider the discriminant information and view consistency information contained in the data itself simultaneously. To solve this problem, a new multi-view dimensionality reduction algorithm, consistent discriminant correlation analysis, is proposed in this paper. The algorithm integrates the class information and the consistency information between views into the dimension reduction process. By maximizing the within-class correlations and the consistency between views, and minimizing the between-class correlations simultaneously, it extracts the low-dimensional features that are more efficient to classification. Furthermore, a kernel consistent discriminant correlation analysis is proposed. The experimental results on several data sets demonstrate the effectiveness of the proposed methods.