Low-rank matrix factorization with multiple Hypergraph regularizer

This paper presents a novel low-rank matrix factorization method, named MultiHMMF, which incorporates multiple Hypergraph manifold regularization to the low-rank matrix factorization. In order to effectively exploit high order information among the data samples, the Hypergraph is introduced to model the local structure of the intrinsic manifold. Specifically, multiple Hypergraph regularization terms are separately constructed to consider the local invariance; the optimal intrinsic manifold is constructed by linearly combining multiple Hypergraph manifolds. Then, the regularization term is incorporated into a truncated singular value decomposition framework resulting in a unified objective function so that matrix factorization is changed into an optimization problem. Alternating optimization is used to solve the optimization problem, with the result that the low dimensional representation of data space is obtained. The experimental results of image clustering demonstrate that the proposed method outperforms state-of-the-art data representation methods.