Graph regularized multiview marginal discriminant projection.

Highlights • This paper proposes a novel supervised multi-view learning algorithm named MMDP, which considers both inter-view and intra-view discriminant information and can maintain the geometric construction of the data manifold. • This paper improves the performance of MMDP via imposing a graph as regularization term to give a penalization of the data geometric structure breaking and generalize MMDP to GMMDP. • The extensive experiments on face recognition tasks have confirmed the effectiveness and robustness of the proposed MMDP and GMMDP methods. Abstract Multi-view data has become commonplace in today's computer vision applications, for the same object can be sampled through various viewpoints or by different instruments. The large discrepancy between distinct even heterogenous views bring the challenge of handling multi-view data. To obtain intrinsic common representation shared by all views, this paper proposes a novel multi-view algorithm called Multiview Marginal Discriminant Projection (MMDP), which is a supervised dimensionality reduction method for searching latent common subspace across multiple views. MMDP takes both inter-view and intra-view discriminant information into account and can preserve the global geometric structure and local discriminant structure of data manifold. Furthermore, the performance of MMDP is improved via imposing graph embedding as a regularization term to give a penalization of the local data geometric structure violation, which is called Graph regularized Multiview Marginal Discriminant Projection (GMMDP). The extensive experimental results on face recognition tasks demonstrate the effectiveness and robustness of MMDP and GMMDP. Finally, this paper excavates a new application scenario of multi-view learning and introduce it including the proposed GMMDP into solving hyperspectral image classification (HIC) problem, which leads to a satisfactory result. [ABSTRACT FROM AUTHOR]