Discriminative Multiple Canonical Correlation Analysis for Information Fusion

TL;DR

This paper introduces DMCCA, a novel discriminative method for multimodal data fusion that enhances class discrimination and reduces computational cost, outperforming existing CCA-based techniques in recognition tasks.

Contribution

The paper proposes DMCCA, a unified framework that improves multimodal information fusion by maximizing within-class and minimizing between-class correlations, with demonstrated superior performance.

Findings

DMCCA outperforms traditional fusion methods in recognition tasks.

Analytical prediction of optimal projected dimension improves efficiency.

Unified framework encompasses CCA, MCCA, and DCCA.

Abstract

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.