Local kernels based graph learning for multiple kernel clustering

Abstract

Multiple kernel clustering (MKC) has been extensively studied in recent years. The focus of MKC is how to explore the information of base kernels. Although existing methods have promising leaning abilities, they ignore the intrinsic local structure contained in base kernels, which may negatively affect their performances. To address the above problem, a novel method, termed as consensus graph learning based on local kernels (CGLLK), is introduced. CGLLK is based on the partitions extracted by kernel k-means. Specifically, we first design a simple yet effective scheme to construct the local kernels of base kernels and then a consensus graph is applied to capture the complementary information contained in the extracted partitions of local kernels. CGLLK also considers the prior knowledge existing in base kernels. Since the partitions of local kernels and the learning stage of the consensus graph contain useful information for each other, the two processes are optimized jointly. Extensive experiments on some popular datasets are carried out to verify the effectiveness of the proposed method. Experimental results illustrate that CGLLK is much more competitive than the state-of-the-art algorithms.