Unifying Latent Tensorized Graph Learning and Representation-Induced View Recovery for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to address the challenge of discovering consistent information across heterogeneous multi-view data with missing views, a scenario frequently encountered in real-world applications. Although several remarkable and representative methods have been proposed to recover the missing information, they often suffer from either suboptimal graph construction or inaccurate view recovery, constrained by the lack of well-connected representations that can simultaneously support consistent structural learning and accurate view completion. In this paper, we propose a novel framework, Tensorized Graph learning with Representation-induced Smooth recovery, which unifies latent tensorized graph learning and representation-induced view recovery in a joint optimization scheme. Specifically, the tensorized graph learning captures high-order cross-view structural correlations, while the smooth representation learning guides accurate recovery of missing views through low-rank and manifold-preserving constraints. This synergy enables robust representation learning. We derive efficient closed-form solutions for each subproblem, ensuring monotonic convergence. Extensive experiments on eight real-world multi-view datasets demonstrate that TGRS-IMVC consistently outperforms state-of-the-art IMVC methods across a range of missing ratios. Ablation studies and parameter sensitivity analyses further validate the effectiveness and stability of the proposed components.