Scalable Multi-View Clustering via Latent Tensor Low-Rank Representation

Existing TLRR(Tensor low-rank representation) methods typically use the observed tensor itself as the dictionary to learn robust representations. However, when the observed data are limited and contaminated by noise, such a self-representation strategy is unable to adequately capture the underlying tensor subspace structure, resulting in degraded performance. To overcome this limitation, we propose Latent Tensor Low-Rank Representation (LT-LRR), a general tensor representation framework that augments the observed tensor dictionary with an unobserved latent component. We show that the resulting latent tensor self-representation admits a closed-form structural characterization under the t-product framework, which naturally yields low-rank constraints on both the representation tensor and the latent correction tensor. Building upon this formulation, we further develop ETRL-SMVC, a scalable multi-view clustering method that applies LT-LRR to anchor graph learning by replacing computationally expensive sample-level self-representation with compact dictionary-based tensor factorization over anchor graphs. Extensive experiments on benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art competitors while maintaining favorable scalability.