Multi-kernel graph fusion for spectral clustering

doi:10.1016/j.ipm.2022.103003

Multi-kernel graph fusion for spectral clustering

2022-09-01

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DOI：10.1016/j.ipm.2022.103003

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Abstract

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Many methods of multi-kernel clustering have a bias to power base kernels by ignoring other kernels. To address this issue, in this paper, we propose a new method of multi-kernel graph fusion based on min-max optimization (namely MKGF-MM) for spectral clustering by making full use of all base kernels. Specifically, the proposed method investigates a novel min-max weight strategy to capture the complementary information among all base kernels. As a result, every base kernel contributes to the construction of the fusion graph from all base kernels so that the quality of the fusion graph is guaranteed. In addition, we design an iterative optimization method to solve the proposed objective function. Furthermore, we theoretically prove that our optimization method achieves convergence. Experimental results on real medical datasets and scientific datasets demonstrate that the proposed method outperforms all comparison methods and the proposed optimization method achieves fast convergence.

Keywords:

Multi-kernel learning

Affinity graph

Self-expression

Spectral clustering

Multi-kernel graph fusion for spectral clustering

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