Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
This addresses a specific issue in kernel-based machine learning for researchers and practitioners, offering an incremental improvement over existing MKL methods.
The paper tackled the problem of multiple kernel learning (MKL) underperforming due to suboptimal kernel combinations and weight assignments, proposing a self-weighted MKL framework that automatically assigns weights based on proximity to a consensus kernel, resulting in verified superiority on benchmark datasets for graph-based clustering and semi-supervised classification.
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.