Discriminative Multiple Canonical Correlation Analysis for Information Fusion
This work addresses multimodal data analysis for applications such as pattern recognition, offering an incremental improvement by unifying existing CCA variants into a single framework.
The paper introduces Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information fusion, which improves discriminative feature extraction by maximizing within-class and minimizing between-class correlations, leading to superior performance in tasks like handwritten digit and human emotion recognition compared to traditional methods.
In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion. DMCCA is capable of extracting more discriminative characteristics from multimodal information representations. Specifically, it finds the projected directions which simultaneously maximize the within-class correlation and minimize the between-class correlation, leading to better utilization of the multimodal information. In the process, we analytically demonstrate that the optimally projected dimension by DMCCA can be quite accurately predicted, leading to both superior performance and substantial reduction in computational cost. We further verify that Canonical Correlation Analysis (CCA), Multiple Canonical Correlation Analysis (MCCA) and Discriminative Canonical Correlation Analysis (DCCA) are special cases of DMCCA, thus establishing a unified framework for Canonical Correlation Analysis. We implement a prototype of DMCCA to demonstrate its performance in handwritten digit recognition and human emotion recognition. Extensive experiments show that DMCCA outperforms the traditional methods of serial fusion, CCA, MCCA and DCCA.