Semi-Supervised and Semi-Paired Graph Regularized Multiset Canonical Correlation Analysis

Multiset canonical correlation analysis (MCCA) plays a key role in analyzing linear correlations among multimodal data. However, when facing semi-supervised and semi-paired multimodal data which widely exist in real world. MCCA normally performs poorly because it requires paired information among different models. At the same time, it fails to exploit the discriminative information due to the fact that it is an unsupervised dimension reduction method. In this paper, we propose a novel algorithm, named semi-supervised semi-paired graph regularized multiset canonical correlation analysis(SSGMCCA). SSGMCCA employs a small amount of paired data to perform MCCA and simultaneously utilize both the global structural information captured from the unlabeled data and the local structural information captured from the labeled data to compensate the limited paired. As a result, SSGMCCA can find the directions which not only maximal correlation among the multimodal data but also maximal separability of the labeled data. Experimental results illustrate the effectiveness of the proposed algorithm.