Abstract: Hypergraph spectral clustering method attracts much attention,because it can effectively describe high-order information among the data．Different from traditional graph model,hyperedge in hypergraph is not a pair-wise link between two data points,while it is a subset of data points sharing with some attribute.In practices,hyperedge is usually built by simple K-NN clustering,so it does not consider inherent relationship among the data.We proposed a new hypergraph spectral clustering algorithm with sparse representation．For each data point,sparse representation was used to seek its related neighbors to form a hyperedge,so the data points in a hyperedge have strong dependency．Finally,the spectral decomposition was performed on the Laplace matrix of the hypergraph to obtain the clustering result．Extensive experiments on face database and handwriting database demonstrate the effectiveness of the proposed method.

Key words: Hypergraph,Sparse representation,Spectral clustering