A Novel Discriminative Framework Integrating Kernel Entropy Component Analysis and Discriminative Multiple Canonical Correlation for Information Fusion

The effective interpretation and integration of multiple information content are important for the efficacious utilisation of multimedia in a wide variety of application context. The major challenge in information fusion lies in the difficulty of identifying the complementary and discriminatory representations from individual channels or data sources. In this paper, we propose a novel framework integrating kernel entropy-estimation and discriminative multiple canonical correlation (DMCC) to address this challenge. Not only the distribution and complementary representations of input data are revealed by entropy estimation, but also the discriminative representations are considered by DMCC, achieving improved recognition accuracy. The effectiveness of the proposed method is demonstrated on two audio emotion databases. Experimental results show that it outperforms the existing methods based on similar principles.