Collaborative structure and feature learning for multi-view clustering

Multi-view clustering divides similar objects into the same class through using the fused multiview information. Most multi-view clustering methods obtain clustering result by only analyzing structure relationship among samples, ignoring the analysis of intrinsic features of each sample, while a few methods operate the original feature on the corresponding high-dimensional kernel matrices. However, noisy and redundant features of samples are inevitably mixed in original multi-view data or high-dimensional kernel matrices. To address this problem, we propose a novel multiview clustering method, which unifies structure learning and feature learning to a framework. Specifically, we obtain a consensus structure information from multiple views via sparse subspace structure learning with weight tensor nuclear norm constraint. Then our feature learning seeks projection directions to obtain data representation by data pseudo labels, which are obtained via the fused consensus structure information. Furthermore, we use the manifold regularization term to establish the relationship between data structure information and learnt data presentation. At last, the two subtasks are alternately iterated and optimized to acquire accurate structure and discriminative data presentation. Experimental results on different datasets validate the proposed method is superior to the state-of-the-art methods.