C2DMCP: View-consistent collaborative discriminative multiset correlation projection for data representation

Multiset canonical correlation analysis (MCCA) is a powerful technique for multi-view joint dimensionality reduction by maximizing linear correlations among the projections. However, most existing MCCA-related methods fail to discover the intrinsic discriminating structure among data spaces and the correspondence between multiple views. In order to address these problems, we incorporate the collaborative representation structure of data points in each view. Then we construct a view-consistent collaborative multiset correlation projection (C2MCP) framework, in which the structures among different views are guaranteed to be consistent and preserved in low-dimensional subspaces. Also, by taking within-class and between-class collaborative reconstruction into account to improve discriminative power for the supervised scenario, we then propose a novel algorithm, called view-consistent collaborative discriminative multiset correlation projection (C2DMCP), to explicitly consider both between-set cumulative correlations and discriminative structure in multiple representation data. The feasibility and effectiveness of the proposed method has been verified on three benchmark databases, i.e., ETH-80, AR and Extended Yale B, with promising results.