  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.
