Semi-supervised Multi-view Clustering with Active Constraints

Multi-view clustering has attracted increasing attention in recent years. However, most existing multi-view clustering approaches are performed in a purely unsupervised manner, while ignoring the valuable weak supervision information that can be obtained (e.g., active query) in many real applications. This paper considers the weak pairwise constraints among samples to enhance the clustering performance, and proposes a Semi-supervised Multi-view Clustering method with Active Constraints, SMCAC for short. SMCAC consists of two stages, clustering (C-stage) and active query (A-stage). In the C-stage, we design a tensor based multi-view graph learning model equipped with sample pairwise constraints regularization to facilitate the discriminative graph learning and fusion. An effective optimization algorithm based on alternating direction minimization is devised to solve the clustering model. In the A-stage, the most uncertain or difficult sample pairs are actively selected to query the constraints, based on the divergence of multi-view similarities learned in the C-stage. The two processes alternate iteratively until the maximum number of queries is reached. Extensive experiments on several popular datasets well validate the effectiveness of the proposed method.