Kernel learning for local learning based clustering

Abstract

For most kernel-based clustering algorithms, their performance will heavily hinge on the choice of kernel. In this paper, we propose a novel kernel learning algorithm within the framework of the Local Learning based Clustering (LLC) (Wu and Schölkopf 2006). Given multiple kernels, we associate a non-negative weight with each Hilbert space for the corresponding kernel, and then extend our previous work on feature selection (Zeng and Cheung 2009) to select the suitable Hilbert spaces for LLC. We show that it naturally renders a linear combination of kernels. Accordingly, the kernel weights are estimated iteratively with the local learning based clustering. The experimental results demonstrate the effectiveness of the proposed algorithm on the benchmark document datasets. © 2009 Springer Berlin Heidelberg.