Tensor-based multi-view clustering is a popular approach. It can enhance representation learning by exploring higher-order correlations among views. However, two key issues remain unsolved. First, minimizing the tensor rank is a complex multi-objective optimization problem, so finding a suitable optimization strategy is an open problem. Moreover, most tensor methods require two phases to obtain the consensus matrix, which usually leads to suboptimal performance. To address these issues, we propose a Tensorial Multi-view Clustering via Alternative Rank Minimization and Inter-view Alignment (ARIA), in which multiple low-rank matrices and the consistent matrix are jointly optimized in a unified framework. Specifically, we stack the representations obtained from different views into a higher-order tensor. Then, a non-convex alternative rank-minimizing regularization is introduced to achieve a tighter approximation of the rank function. Besides, we impose intra-view alignment constraints to establish a connection between inter-view and intra-view. Unlike the previous method, it is a one-step strategy to obtain the consensus representation. Notably, our approach requires only linear complexity, and thus it can be successfully applied in large-scale clustering tasks. Extensive experiments validate the effectiveness and scalability of the proposed method. The code for ARIA is publicly available athttps://github.com/zskong/ARIA.

Deep multi-view clustering (MVC) has gained widespread attention as it can effectively mine consistent information from multiple views and improve clustering performance. However, view bias often exists between views (i.e., the quality differences between views). Treating all views equally inevitably destroys structural information when simply concatenating or summing the embedded representation of multiple views. To alleviate this issue, we propose a deep multi-view clustering with intra-view similarity and cross-view correlation learning (MISCC), facilitating the intra-view discriminability and inter-view complementarity. Specifically, we utilize the intra-view inherent structure information to dynamically identify semantically similar samples within each view. By aggregating their embedding representations, fine-grained structures are enhanced to boost intra-cluster compactness and inter-cluster separation. Then, we construct a cross-view correlation learning module to align semantically related views while preserving the distinctive features of irrelevant views. Based on them, a centralized clustering alignment strategy is proposed to align the similarity distribution and clustering structure between each view and the unified view, balancing the diverse information among multiple views. By jointly training these modules, the unified representation is optimized to capture more discriminative information from multiple views. Extensive experiments conducted on eleven multi-view datasets demonstrate that MISCC outperforms the state-of-the-art clustering methods.

Multi-view clustering (MVC) has gained extensive attention for its capacity to handle heterogeneous data. However, current autoencoder-based MVC methods suffer from a limitation: embedding space exhibits severe imbalances in the efficacy of feature direction, creating a long-tailed singular value distribution where few directions dominate. To mitigate this, we introduce a novel Activate-Then-Eliminate Strategy for Multi-View Clustering (AEMVC), inspired by the observation that balanced feature directions can facilitate enhancing discrimination of learned representations. AEMVC dynamically adjusts the contributions of different feature directions through two keys: a Feature Activation Module that narrows singular value discrepancies to prevent dominant directions from controlling clustering decisions, and an Inter-view Mutual Supervision strategy that filters redundant information by adaptively determining view-specific thresholds based on cross-view consistency. By activating more feature directions and eliminating each view's adverse factors, AEMVC achieves more balanced and discriminative embedding representations. Extensive experiments on seven multi-view benchmarks validate AEMVC's effectiveness, demonstrating substantial improvements over state-of-the-art methods.