Composite Nonlinear Multiset Canonical Correlation Analysis For Multiview Feature Learning And Recognition

In this paper, we propose a composite nonlinear multiset canonical correlation projections (CNMCPs) framework where orthogonal constraints are imposed in each set. This makes CNMCP capable of learning uncorrelated low-dimensional features with minimum redundancy in Hilbert space. With the CNMCP framework, we further present a particular algorithm called multikernel multiset canonical correlations or mKMCC, which introduces different weights into multiple nonlinear functions in all views. An alternating iterative optimization is designed for computational solution. Numerous experimental results on practical datasets have demonstrated the effectiveness and robustness of mKMCC, in contrast with existing kernel correlation learning approaches.