Multiview emotion recognition via multi-set locality preserving canonical correlation analysis

In this paper, we propose a novel Multi-set Locality-Preserving Canonical Correlation Analysis (MLPCCA) for multi-view learning and fusion. The proposed MLPCCA captures the intrinsic structure of data while it learns the optimum basis for maximizing the correlation among different sets of data. To verify the effectiveness of the proposed technique, the proposed MLPC A has been applied in audio-based emotion recognition and visual-based emotion recognition, respectively. The experimental results demonstrated that the proposed MLPCCA can achieve a higher recognition accuracy compared to the existing methods including CCA, LPCCA, and MCCA.

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