Semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization

Abstract

Nonnegative matrix factorization (NMF) is a very effective method for high dimensional data analysis, which has been widely used in computer vision. However, the conventional NMF is unsupervised, and thus, it cannot utilize the label information. To this end, semi-supervised NMF is proposed, which performs the NMF with the guidance of the supervisory information. However, semi-supervised NMF fails to make full use of label information, which limits the performance of clustering using low dimensional representation, and samples and features cannot be clustered in a mutually reinforcing manner. To solve the above shortcomings, we propose a semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization model (SSCCDS). First, co-clustering is introduced to cluster samples and features simultaneously. Secondly, the model imposes similarity and dissimilarity regularization constraints on samples by low-dimensional representations. Specifically, similarity and dissimilar regularization constraints are imposed on labeled samples, and similarity regularization constraints are imposed on unlabeled data. Thirdly, the model proposes sparse neighbor constraints for feature consistent learning. Then, a multiplicative alternating scheme is proposed for objective optimization. A large number of experiments on different data sets show that SSCCDS has good clustering ability.