TLRLF4MVC: Tensor Low-Rank and Low-Frequency for Scalable Multi-View Clustering

Anchor-based multi-view clustering has garnered much attention for its effectiveness in handling massive datasets. However, current methods either fail to consider intra-view similarity or require ($\mathcal {O}(N^{3})$O(N3)) for exploring intra-view similarity, making efficient large-scale multi-view clustering difficult. This paper introduces a novel tensor low-frequency component (TLFC) operator, which achieves smooth representation among samples. Furthermore, this TLFC operator, which explores intra-view similarity, incorporates tensor nuclear norm (TNN) operator and consensus regularization that explore inter-view correlations, resulting in the development of tensor low-rank and low-frequency for scalable multi-view clustering (TLRLF4MVC). Iteratively, as intra-view sample similarity and complementary information across views achieve balance, the learned embedding features are mapped into a smooth and compact subspace, ultimately leading to outstanding clustering performance. Extensive experiments on six large-scale multi-view datasets demonstrate that TLRLF4MVC not only significantly outperforms state-of-the-art methods in terms of clustering accuracy but also achieves remarkable computational efficiency, particularly when handling massive data.