Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clustering
This work addresses the problem of handling missing data in multiview clustering for real-world applications, representing an incremental advancement by integrating inter-view correlations into existing tensor-based approaches.
The paper tackles incomplete multiview clustering by proposing a joint tensor and inter-view low-rank recovery method, which improves clustering accuracy and robustness compared to state-of-the-art methods, as demonstrated through experiments on synthetic and real-world datasets.
Incomplete multiview clustering (IMVC) has gained significant attention for its effectiveness in handling missing sample challenges across various views in real-world multiview clustering applications. Most IMVC approaches tackle this problem by either learning consensus representations from available views or reconstructing missing samples using the underlying manifold structure. However, the reconstruction of learned similarity graph tensor in prior studies only exploits the low-tubal-rank information, neglecting the exploration of inter-view correlations. This paper propose a novel joint tensor and inter-view low-rank Recovery (JTIV-LRR), framing IMVC as a joint optimization problem that integrates incomplete similarity graph learning and tensor representation recovery. By leveraging both intra-view and inter-view low rank information, the method achieves robust estimation of the complete similarity graph tensor through sparse noise removal and low-tubal-rank constraints along different modes. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed approach, achieving significant improvements in clustering accuracy and robustness compared to state-of-the-art methods.