Deep graph regularized non-negative matrix factorization for multi-view clustering

Abstract

Multi-view clustering is an unsupervised method which aims to enhance the clustering performance by combining the knowledge from multiple view data. Non-negative matrix factorization (NMF) is one of the most favourable multi-view clustering methods due to its strong representation ability of non-negative data. However, NMF only factorizes the data matrix into two non-negative factor matrices, which may limit its ability to learn higher level and more complex hierarchical information. To overcome this shortcoming, in this paper, we propose a multi-view clustering method based on deep graph regularized non-negative matrix factorization (MvDGNMF). MvDGNMF is able to extract more abstract representation by constructing a multilayer NMF model with graph Laplacian regularization and drive the last layer representation from each view to a common consensus representation. Meanwhile, an efficient algorithm using alternating multiplicative update rules is developed. Furthermore, in order to demonstrate the effectiveness of this proposed method, we employ several open datasets including image and text datasets to evaluate the clustering performance of MvDGNMF and the state-of-art methods.