Tensor Completion Framework by Graph Refinement for Incomplete Multi-View Clustering

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Huibing Wang, Yawei Chen, Mingze Yao, Wenzhe Liu, Jinjia Peng, Xianping Fu

Published: 2025, Last Modified: 05 Mar 2026IEEE Trans. Multim. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Incomplete Multi-view Clustering (IMVC) endeavors to harness information from multiple incomplete views to partition multi-view data into their respective clusters. How to recover missing information with lossless fidelity is the core of IMVC, which is of vital importance but challenging. Most of the existing methods include a feature recovery step to mitigate the negative impact of missing samples on the feature graph, however, these IMVC algorithms simply utilize the correlation between samples to recover the relationship between the unmissing instances and the missing instances while ignoring the consistency between views, which leads to often unsatisfactory recovery results. In addition, previous IMVC algorithms focus more on the recovery of incomplete data, ignoring the effect of the error term on incomplete graphs. This can mislead the recovery process of IMVC algorithm and the feature graph can be affected by anomalous information, which leads to degradation of clustering performance. To address this gap, this paper introduces the Tensor Completion Framework by Graph Refinement for Incomplete Multi-view Clustering (IMVC-TGR). IMVC-TGR separates the redundant information in each affine graph by graph refinement operation, aiming to mitigate the negative impact of error terms and redundant information on the feature graph during the recovery process. Meanwhile, IMVC-TGR stacks the feature graphs into tensors to explore intra-view correlation and inter-view consistency, so as to recover the relationship between missing samples and non-missing samples, and improve the quality of the feature graphs. Finally, IMVC-TGR introduces semantic consistency constraints and self-weighted fusion strategies into the high-quality feature graphs, aiming at preserving the complementary information between different views while balancing the contributions of the refined representation matrices of different views. The experimental results on multiple different datasets indicate that IMVC-TGR can achieve state-of-the-art performance.