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.

多核学习（MKL），结合一组预先指定的基本核来提高聚类性能，已成为一个重要的研究课题。不幸的是，目前的方法在嘈杂的环境中存在以下缺陷。1）由于内核中的噪声，它们的聚类性能可能会显着降低，这是由于缺乏对基本内核组合的可靠判别指南造成的。2）来自损坏数据或遮挡的噪声可能会破坏他们获得的亲和矩阵的块对角结构，这将影响使用谱聚类时的聚类性能。在这项工作中，为了解决上述问题，我们提出了一种基于自动加权多核学习的鲁棒子空间聚类（AWLKSC）算法。该模型集成了多核学习策略，Correntropy-Induced Metric (CIM)、低秩逼近技术和块对角线约束。此外，开发了一种通过交替最小化和广义软阈值集成的有效 AM&GST 算法来优化 AWLKSC。实验中考虑了七种类型的噪声，实验结果表明 AWLKSC 比几种最新的单核和多核聚类方法更有效和鲁棒。