Multi-view clustering via matrix factorization assisted k-means

Matrix factorization based multi-view clustering algorithms has attracted much attention in recent years due to the strong interpretability and efficient implementation. In general, these approaches firstly compute the coefficient matrices of each data views, and learn a consensus matrix simultaneously. By applying the classical clustering techniques, such as k-means, on the consensus matrix, the final partition can be easily obtained. However, most of previous models work in a "step-by-step" manner, which cannot perform multi-view matrix factorization and clustering label generation simultaneously, leading to degenerated performance. In this paper, we propose a novel "one-pass" method, which integrates matrix factorization and k-means into a unified framework, named multi-view clustering via matrix factorization assisted k-means (MFK). In MFK, the generation of cluster indicator matrix and coefficient matrix learning can boost each other, leading to final improved clustering performance. Furthermore, we adopt a graph Laplacian regularization on the indicator matrix in order to capture the intrinsic geometric structure of original data. An alternating optimization strategy is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to demonstrate the superiority and effectiveness of the proposed MFK model.