Hypergraph-Regularized Lp Smooth Nonnegative Matrix Factorization for Data Representation

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph-regularized Lp smooth nonnegative matrix factorization (HGSNMF) by incorporating the hypergraph regularization term and the Lp smoothing constraint term into the standard NMF model. The hypergraph regularization term can capture the intrinsic geometry structure of high dimension space data more comprehensively than simple graphs, and the Lp smoothing constraint term may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of the proposed method is theoretically investigated. The experimental results on five different data sets show that the proposed method has a better clustering effect than the related state-of-the-art methods in the vast majority of cases.

Automated summary

In this paper, a hypergraph-regularized Lp smooth nonnegative matrix factorization (HGSNMF) is proposed by incorporating hypergraph regularization and Lp smoothing constraint into standard NMF model. The method captures the intrinsic geometry structure of high dimension space data comprehensively and provides a smooth and accurate solution to the optimization problem. Experimental results show that the proposed method outperforms the state-of-the-art methods in most cases.