Manifold Adaptive Multiple Kernel K-Means for Clustering

TL;DR

This paper introduces a novel multiple kernel k-means clustering method that incorporates local manifold structures via manifold adaptive kernels, leading to improved clustering performance over existing methods.

Contribution

The paper proposes a manifold adaptive multiple kernel k-means approach that effectively captures local manifold structures, enhancing clustering accuracy.

Findings

Outperforms several state-of-the-art baseline methods

Effectively captures local manifold structures in kernels

Improves clustering performance on various datasets

Abstract

Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the nonlinear relationship within kernels, whereas the local manifold structure among multiple kernel space is not sufficiently considered. In this paper, we adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. Thus, the induced multiple manifold adaptive kernels not only reflect the nonlinear relationship but also the local manifold structure. We then perform multiple kernel clustering within the multiple kernel k-means clustering framework. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.