Efficient Multiple Kernel k-Means Clustering With Late Fusion

The recently proposed multiple-kernel clustering algorithms have demonstrated promising performance in various applications. However, most of the existing methods suffer from high computational complexity and intensive time cost. To address this issue, we propose to fulfill multiple kernel k-means clustering via a late fusion manner. In specific, we design two multiple kernel k-means algorithms with late fusion, whose computational complexities linearly grow with the number of samples. The proposed algorithms integrally optimize the various clustering matrices into the optimal consensus clustering results iteratively. Furthermore, we analyze the computational complexities of the proposed algorithms and theoretically prove their convergence. As demonstrated by the experiments on six benchmark datasets, our algorithms achieve comparable or better clustering performance to state-of-the-art ones with less time cost, which demonstrates the advantages of the late fusion in multiple kernel k-means.