Information fusion based on kernel entropy component analysis in discriminative canonical correlation space with application to audio emotion recognition

As an information fusion tool, Kernel Entropy Component Analysis (KECA) is realized by using descriptor of information entropy and optimized by entropy estimation. However, as an unsuper-vised method, it merely puts the information or features from different channels together without considering their intrinsic structures and relations. In this paper, we introduce an enhanced version of KECA for information fusion, KECA in Discriminative Canonical Correlation Space (DCCS). Not only the intrinsic structures and discriminative representations are considered, but also the natural representations of input data are revealed by entropy estimation, leading to improved recognition accuracy. The effectiveness of the proposed solution is evaluated through experiments on two audio emotion databases. Experimental results show that the proposed solution outperforms the existing methods based on similar principles.