Selecting Discriminative Features with Discriminative Multiple Canonical Correlation Analysis for Multi-Feature Information Fusion

In this paper, it presents a novel approach for selecting discriminative features in multimodal information fusion based discriminative multiple canonical correlation analysis (DMCCA), which is the generalized form of canonical correlation analysis (CCA), multiple canonical correlation analysis (MCCA) and discriminative canonical correlation analysis (DCCA). The proposed approach identifies the discriminative features from the multi-feature in Fractional Fourier Transform (FRFT) domain, which are capable of simultaneously maximizing the within-class correlation and minimizing the between-class correlation, leading to better utilization of the multi-feature information and producing more effective pattern recognition results. The effectiveness of the introduced solution is demonstrated through extensive experimentation on a visual based emotion recognition problem.