Tensor-based multi-view clustering with consistency exploration and diversity regularization

How to make good use of information from all views is the core problem in multi-view clustering (MVC), and widely recognized experience of attaining this goal is to leverage consistent and complementary principles. However, current methods do not take full advantage of these two principles. On the one hand, some existing methods ignore the connection between multiple views and the influence of manifold information on consensus representation. On the other hand, some others simply sum all the view-specific representations together as the final representation, which result in insufficient mining of complementary information. In this manuscript, we propose a novel tensor-based multi-view clustering approach that significantly improves consistency exploration and diversity capturing. In particular, we utilize the low-rank tensor learning with tensor-based singular value decomposition (t-SVD), aiming at capturing the correlations among different views. Moreover, we further split the low-rank tensor into a consistent matrix and a set of view-specific matrices, where the former is responsible for exploring the common manifold information via graph regularization, and the latter is used to mine complementary information through learning the view-specific representations as various as possible. We integrate low-rank tensor learning, consistency exploration and diversity regularization into a whole framework and use an alternating direction minimization strategy to optimize it. Experiments conducted on eight benchmark datasets show that our approach gains superior performance over several state-of-the-arts.