基于低秩张量表示的多视图子空间聚类
Multi-View Subspace Clustering Based on Low-Rank Tensor Representation
DOI: 10.12677/PM.2023.1310294, PDF,    国家自然科学基金支持
作者: 李 欢, 唐科威：辽宁师范大学数学学院，辽宁 大连
关键词: 子空间聚类；低秩张量表示；Frobenius范数；张量核范数；Subspace Clustering； Low-Rank Tensor Representation； Frobenius Norm； Tensor Core Norm
摘要: 近年来，多视图子空间聚类是一个热点话题，基于低秩张量的方法受到广泛关注。为了更好地挖掘不同视图间的高阶关联性，本文采用最新基于t-SVD的张量核范数，使用系数矩阵的核范数和Frobenius范数作为正则项。在PIE、ORL、MSRA和MNIST四个数据集上与流行的子空间聚类算法的对比试验表明，本文提出的算法是一个有效的方法。
Abstract: In recent years, multi-view subspace clustering has been a hot topic, and methods based on low-rank tensors have received widespread attention. In order to better mine the high-order cor-relation between different views, this paper adopts the latest tensor kernel norm based on t-SVD, using the kernel norm and Frobenius norm of the coefficient matrix as regularization terms. Comparative experiments with popular subspace clustering algorithms on four data sets: PIE, ORL, MSRA and MNIST show that the algorithm proposed in this article is an effective method.
文章引用：李欢, 唐科威. 基于低秩张量表示的多视图子空间聚类[J]. 理论数学, 2023, 13(10): 2877-2887. https://doi.org/10.12677/PM.2023.1310294

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