Robust subspace clustering based on automatic weighted multiple kernel learning

Abstract

Abstract Multiple kernel learning (MKL), which combines a set of prespecified basic kernels to improve the clustering performance, has become an important research topic. Unfortunately, the current methods have the following defects in noisy circumstances. 1) Their clustering performance may be significantly reduced due to the noise in the kernel, which is caused by the lack of a reliable discriminant guideline for basic kernel combinations. 2) The noise from corrupted data or occlusion may destroy the block-diagonal structures of the affinity matrices they obtained, which will affect the clustering performance when using spectral clustering. In this work, to solve the above problems, we propose an automatic weighted multikernel learning-based robust subspace clustering (AWLKSC) algorithm. The model integrates multikernel learning strategies, the Correntropy-Induced Metric (CIM), low rank approximation technology and block diagonal constraints. In addition, an effective AM&GST algorithm, which is integrated by alternating minimization and generalized soft-thresholding, is developed to optimize the AWLKSC. Seven types of noise are considered in the experiments, and the experimental results illustrate that AWLKSC is more effective and robust than several up-to-date single kernel and multiple kernel clustering methods.