Enhanced and Scalable Latent Multi-View Subspace Clustering

Latent representations have demonstrated significant effectiveness in multi-view subspace clustering (MVSC). However, existing latent MVSC methods usually suffer from high time complexity—typicallyO(n3) fornsamples—which restricts their application to large-scale data. Moreover, the self-representation matrix relies heavily on the recovery quality of the latent subspace representation, potentially leading to insufficient learning of subspace structures across different views. To address these limitations, this paper proposes an Enhanced and Scalable Latent Multi-view Subspace Clustering method, termed ESLMSC. Specifically, ESLMSC constructs a compact representation matrix via anchor learning to replace the computationally expensive full self-representation matrix. Meanwhile, the compact representation matrix jointly learns subspace structures from both the recovered latent subspace representation and the original data matrix of each view, whereby its comprehensive representational ability is strengthened. Furthermore, multiple anchor projection matrices of different dimensions enhance the learning of complementary information in the recovered latent subspace representation through a hierarchical descent manner. Finally, with a fast alternating optimization algorithm, we can obtain an enhanced subspace representation matrix for clustering. Extensive experiments on diverse multi-view benchmark datasets, including several large-scale ones, demonstrate that ESLMSC consistently achieves superior performance over state-of-the-art MVSC methods.