Discriminative Multiple Canonical Correlation Analysis for Multi-feature Information Fusion

Abstract

This paper presents a novel approach for multi-feature information fusion. The proposed method is based on the Discriminative Multiple Canonical Correlation Analysis (DMCCA), which can extract more discriminative characteristics for recognition from multi-feature information representation. It represents the different patterns among multiple subsets of features identified by minimizing the Frobenius norm. We will demonstrate that the Canonical Correlation Analysis (CCA), the Multiple Canonical Correlation Analysis (MCCA), and the Discriminative Canonical Correlation Analysis (DCCA) are special cases of the DMCCA. The effectiveness of the DMCCA is demonstrated through experimentation in speaker recognition and speech-based emotion recognition. Experimental results show that the proposed approach outperforms the traditional methods of serial fusion, CCA, MCCA and DCCA.