Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high‐dimensional data, whose noise goes against the clustering results. Besides, some methods based on self‐representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel‐based Low‐rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high‐dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low‐rank tensor is learned in the process of exploring the high‐order correlations between data. Experiments on real‐world data sets show that our method not only yields better results but also is quite time‐saving compared with other state‐of‐the‐art models.

中文翻译：

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基于内核的低秩张量多视图谱聚类

多视图谱聚类旨在通过使用多视图信息有效地将数据分成不同的簇。许多研究从原始高维数据中学习亲和度矩阵，其噪声与聚类结果背道而驰。此外，一些基于自表示子空间聚类的方法具有很高的时间复杂度。在本文中，我们提出了一种简单但有效且高效的方法，称为基于内核的低秩张量多视图频谱聚类（KLTMSC）来解决这些问题。KLTMSC 不是使用原始数据来获取亲和度矩阵，而是从高维数据的核表示中学习亲和度矩阵以减少噪声信息。此外，为了对噪声具有鲁棒性，在探索数据之间的高阶相关性的过程中学习了低秩张量。