Multi-view Common Component Discriminant Analysis for Cross-view Classification

Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision. A promising approach to handle this problem is the multi-view subspace learning (MvSL), which intends to find a common subspace for multi-view data. Despite the satisfactory results achieved by existing methods, the performance of previous work will be dramatically degraded when multi-view data lies on nonlinear manifolds. To circumvent this drawback, we propose Multi-view Common Component Discriminant Analysis (MvCCDA) to handle view discrepancy, discriminability and nonlinearity in a joint manner. Specifically, our MvCCDA incorporates supervised information and local geometric information into the common component extraction process to learn a discriminant common subspace and to discover the nonlinear structure embedded in multi-view data. We develop a kernel method of MvCCDA to further boost the performance of MvCCDA. Beyond kernel extension, optimization and complexity analysis of MvCCDA are also presented for completeness. Our MvCCDA is competitive with the state-of-the-art MvSL based methods on four benchmark datasets, demonstrating its superiority.