Multi-view hypergraph learning by patch alignment framework

Graph-based methods are currently popular for dimensionality reduction. However, most of them suffer from over-simplified assumption of pairwise relationships among data. Especially for multi-view data, different relationships from different views are hard to be integrated into a single graph. In this paper, we propose a novel semi-supervised dimensionality reduction method for multi-view data. First, we assume the hyperedges in hypergraph as patches and apply hypergraph to the patch alignment framework. Second, the weights of the hyperedges are computed with statistics of distances between neighboring pairs and the patches from different views are integrated. In this way, we construct Multi-view Hypergraph Laplacian matrix and we get the dimensionality-reduced data by solving the standard eigen-decomposition to obtain the projection matrix. The experimental results demonstrate the effectiveness of the proposed method on retrieval performance.