Enhanced Multi-view Fuzzy Clustering Using Double Visible-hidden View Cooperation and Network LASSO Constraint

Multi-view clustering is an important topic in multi-view learning, where the cooperation of different views is used to improve clustering performance. Although multi-view clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: (i) insufficient exploitation of cooperative learning between visible and hidden information despite some preliminary attempts, and (ii) inadequate consideration of topological information for improving multi-view clustering. To meet the challenges, we propose the cooperation enhanced multi-view fuzzy clustering method (CE-MVFC) in this paper. First, we characterize multi-view data with two hidden views which are obtained by adaptive multi-view non-negative matrix factorization (NMF) and fuzzy partition information of each sample in different clusters. Then, we integrated the hidden views and the original visible views to realize visible-hidden cooperation learning. Furthermore, we establish a similarity matrix for each visible view and the hidden view obtained through NMF to describe the data topology in these views. Based on the spatial topological relationship of the samples and the representation of hidden view obtained by fuzzy partition, the network least absolute shrinkage and selection operator (LASSO) is constructed to constrain multi-view learning. Finally, we develop the multi-view clustering method by exploiting the visible-hidden information cooperation and the spatial topological information constraints. Experiments on benchmark multi-view datasets are conducted to demonstrate the highly competitive performance of the proposed CE-MVFC against the state-of-the-art methods.