Low-rank-aware deep clustering for large-scale multiview data

Multi-view clustering integrates complementary signals from multiple heterogeneous views to reveal hidden grouping structures. It has become increasingly relevant in large-scale practical scenarios, such as recommendation systems and multimedia processing. Although considerable progress has been achieved, many deep multiview clustering approaches still struggle to effectively capture the underlying data structure: low-rank properties inherent in multiview data are often overlooked in representation learning, cross-view interactions are often constrained by inflexible alignment schemes that restrict view diversity, and scalability is frequently achieved through ad hoc approximations or loosely coordinated components. In this work, we present LADA-Cluster, a low-rank-aware deep clustering framework designed for large-scale multiview data. The proposed approach integrates low-rank structure directly into representation learning by projecting view-specific embeddings into a shared low-dimensional subspace while preserving view-dependent information through adaptive alignment. In addition, a structure-aware hierarchical anchor graph fusion mechanism is developed to support efficient and scalable relational modeling. Evaluations conducted on several large-scale real-world multiview datasets indicate that LADA-Cluster delivers consistently strong clustering performance, achieves stronger robustness under noisy and incomplete views, and maintains favorable scalability as the data scale increases, highlighting the effectiveness of low-rank-aware modeling for deep multiview clustering.