A unified multiset canonical correlation analysis framework based on graph embedding for multiple feature extraction

Multiset canonical correlation analysis (MCCA) can simultaneously reduce the dimensionality of multimodal data. Thus, MCCA is very much suitable and powerful for multiple feature extraction. However, most existing MCCA-related methods are unsupervised algorithms, which are not very effective for pattern classification tasks. In order to improve discriminative power for handling multimodal data, we, in this paper, propose a unified multiset canonical correlation analysis framework based on graph embedding for dimensionality reduction (GbMCC-DR). Under GbMCC-DR framework, three novel supervised multiple feature extraction methods, i.e., GbMCC-LDA, GbMCC-LDE, and GbMCC-MFA are presented by incorporating several well-known graphs. These three methods consider not only geometric structure of multimodal data but also separability of different classes. Moreover, theoretical analysis further shows that, in some specific circumstances, several existing MCCA-related algorithms can be unified into GbMCC-DR framework. Therefore, this proposed framework has good expansibility and generalization. The experimental results on both synthetic data and several popular real-world datasets demonstrate that three proposed algorithms achieve better recognition performance than existing related algorithms, which is also the evidence for effectiveness of GbMCC-DR framework.