A Deep Discriminant Fractional-order Canonical Correlation Analysis For Information Fusion

The advance of sensory and computing technology has attracted wide attention in the study of intelligent information fusion for multimedia computing and analysis. As a result, information fusion has been taking center stage in the intelligent multimedia and machine learning communities. In this paper, a deep discriminant fractional-order canonical correlation analysis (DDFCCA) method is proposed with application to information fusion. Benefiting from the integration of deep cascade neural networks (NNs) with discriminant power of the fractional-order correlation matrix across multiple data/information sources, the proposed DDFCCA method demonstrates the ability to generate high quality data/information representation. To verify the effectiveness and generic nature of the proposed method, we conduct experiments on three database (MNIST database, RML audio emotional database, and Caltech101 database). Experimental results validate the superiority of the DDFCCA method over state-of-the-art for information fusion.