Canonical Correlation Discriminative Learning for Domain Adaptation

Abstract

Domain adaptation aims to diminish the discrepancy between the source and target domains and enhance the classification ability for the target samples by using well-labeled source domain data. However, most existing methods concentrate on learning domain invariant features for cross-domain tasks, but ignore the correlation and discriminative information between different domains. If the learned features from the source and target domains are not correlated, the adaptability of domain adaptation methods will be greatly degraded. To make up for this deficiency, we propose a novel domain adaptation approach, referred to as Canonical Correlation Discriminative Learning (CCDL) for domain adaptation. By introducing a novel correlation representation, CCDL maximizes the correlations of the learned features from the two domains as much as possible. Specifically, CCDL learns a latent feature representation to reduce the difference by jointly adapting the marginal and conditional distributions between the source and target domains, and simultaneously maximizes the inter-class distance and minimizes the intra-class scatter. The experiments certify that CCDL is superior to several state-of-the-art methods on four visual benchmark databases.