Multiple kernel clustering with late fusion consensus local graph preserving

Abstract

Multiple kernel clustering (MKC) methods aim at integrating an optimal partition from a set of precalculated kernel matrices. Though achieving success in various applications, we observe that existing MKC methods: (i) lack of representation flexibility; and (ii) do not considerably preserve the locality structure in partition space. These issues may adversely affect the learning procedure of MKC, leading to unsatisfying clustering performance. In this paper, we propose a late fusion MKC method with local graph refinement to address the aforementioned issues. Different from existing MKC mechanisms, our method unifies the traditional weighted multiple kernel k-means, kernel partition, and graph construction into a single optimization procedure. The local graph is utilized to preserve the locality information in partition space and therefore all of the counterparts can be boosted for mutual clustering improvements. By this way, our approach enhances the local graph structure in partition space and enjoys more flexible kernel representations, leading to significant clustering improvements. Moreover, a three-step alternate algorithm is developed to solve the resultant optimization problem with proved convergence. Extensive experiments are conducted on several multiple kernel benchmark datasets to compare the proposed algorithm with the state-of-the-art ones, and the results well demonstrate its effectiveness and superiority.