Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification

Zhao Kang, Xiao Lu, Jinfeng Yi, Zenglin Xu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 2312-2318. https://doi.org/10.24963/ijcai.2018/320

Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.
Keywords:
Machine Learning: Kernel Methods
Machine Learning: Semi-Supervised Learning
Machine Learning: Clustering