Multiview Learning via Deep Discriminative Canonical Correlation Analysis

Abstract

In this paper, we propose Deep Discriminative Canonical Correlation Analysis (DDCCA), a method to learn the nonlinear transformation of two data sets such that the within- class correlation is maximized and the inter-class correlation is minimized. Parameters of the two deep transformations are jointly learned. Unlike CCA and Discriminative CCA, the proposed DDCCA does not need inner product. The proposed DDCCA was evaluated in two applications, handwritten digit recognition and speech-based emotion recognition. The experimental results demonstrated that the proposed DDCCA can get a higher recognition accuracy compared to the existing Deep CCA method.