Multiple kernel clustering with corrupted kernels

Abstract Multiple kernel clustering (MKC) algorithms usually learn an optimal kernel from a group of pre-specified base kernels to improve the clustering performance. However, we observe that existing MKC algorithms do not well handle the situation that kernels are corrupted with noise and outliers. In this paper, we first propose a novel method to learn an optimal consensus kernel from a group of pre-specified kernel matrices, each of which can be decomposed into the optimal consensus kernel matrix and a sparse error matrix. Further, we propose a scheme to address the problem of considerable corrupted kernels, where each given kernel is adaptively adjusted according to its corresponding error matrix. The inexact augmented Lagrange multiplier scheme is developed for solving the corresponding optimization problem, where the optimal consensus kernel and the localized weight variables are jointly optimized. Extensive experiments well demonstrate the effectiveness and robustness of the proposed algorithm.

• Publication year

  2017

• Venue

  Neurocomputing

• Publication date

  2017-12-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1016/j.neucom.2017.06.044
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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