Deep learning-based multi-view clustering (DMVC) has garnered significant attention and achieved remarkable success, primarily due to its powerful nonlinear representation capabilities. However, most existing DMVC methods fall short in effectively modeling the inherent high-order correlations among different views. In contrast, traditional MVC techniques have long recognized the effectiveness of tensor-based approaches in capturing such complex interactions. To bridge this gap, we propose STANCE (multi-view Subspace Tensorization with Attentive Clustering Embedding), a novel DMVC framework that integrates tensor modeling into a deep learning paradigm. Specifically, STANCE first employs view-specific auto-encoders to project raw data into latent feature spaces, enabling robust subspace representations. These representations are then stacked to construct a third-order tensor, upon which a Tensor-based low-rank constraint is imposed to explicitly capture high-order inter-view dependencies. To further enhance consistency across views, STANCE incorporates an attention-based adaptive fusion module that dynamically assigns weights to view-specific representations at the sample level, thereby extracting consistent information from features that are both similar and confident. Comprehensive experiments, encompassing parameter analysis, ablation studies, and visual comparisons, are conducted to thoroughly evaluate the effectiveness of STANCE. The results demonstrate that STANCE significantly outperforms state-of-the-art methods on various benchmark datasets. © 2025 Elsevier Ltd

Multi-view subspace clustering optimizes accuracy through the integration of intrinsic data from diverse views. However, current methods, despite their strong clustering performance, grapple with scalability due to high time complexity. The potential of anchor-based models to address this challenge is noteworthy, yet they often disregard higher-order relationships and crucial complementary insights that exist among views. To tackle these issues, we present a Tensorized Structural Anchor-based Method for Scalable Multi-view Subspace Clustering (TSA-SMSC). Firstly, TSA-SMSC characterizes the high-order collections by minimizing the tensor regularization on the third-order tensor, which comprises anchor graph matrices from different views. More importantly, we propose a novel self-weighted tensor logarithmic Schatten-p function, which enables us to effectively capture structural complementarity by incorporating the distinctions between singular values using adaptive weights. Finally, we design a structural fusion term to constrain connected components in anchor graphs and facilitate consensus representation. Extensive experiments on six large-scale datasets demonstrate the superiority of our proposed framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Tensor regression is a powerful tool for analyzing complex multi-dimensional data in fields such as neuroimaging and spatiotemporal analysis, but its effectiveness is often hindered by insufficient sample sizes. To overcome this limitation, we adopt a transfer learning strategy that leverages knowledge from related source tasks to improve performance in data-scarce target tasks. This approach, however, introduces additional challenges including model shifts, covariate shifts, and decentralized data management. We propose the Low-Rank Tensor Transitions (LoRT) framework, which incorporates a novel fusion regularizer and a two-step refinement to enable robust adaptation while preserving low-tubal-rank structure. To support decentralized scenarios, we extend LoRT to D-LoRT, a distributed variant that maintains statistical efficiency with minimal communication overhead. Theoretical analysis and experiments on tensor regression tasks, including compressed sensing and completion, validate the robustness and versatility of the proposed methods. These findings indicate the potential of LoRT as a robust method for tensor regression in settings with limited data and complex distributional structures. © 2025 by the author(s).