Multi-view clustering via joint nonnegative matrix factorization

Many real-world datasets are comprised of dierent rep-resentations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multi-view clustering algorithms have been developed to clus-ter multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In this paper, we propose a novel NMF-based multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views. The key idea is to formulate a joint matrix factorization process with the constraint that pushes clustering solution of each view towards a common consensus instead of xing it directly. The main challenge is how to keep clustering solutions across dierent views meaningful and comparable. To tackle this challenge, we design a novel and eective normaliza-tion strategy inspired by the connection between NMF and PLSA. Experimental results on synthetic and sev-eral real datasets demonstrate the eectiveness of our approach.