Document Type

Conference Paper

Publication Date

2025

DOI

10.1145/3746027.3755365

Publication Title

MM '25: Proceedings of the 33rd ACM International Conference on Multimedia

Pages

1686-1694

Conference Name

33rd ACM International Conference on Multimedia, October 27-31, 2025, Dublin, Ireland

Abstract

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a view feature projector that learns view-specific features and projects them into a common feature space; 2) an energy-guided selective imputation module that identifies the most reliable source view for each view based on view energies, and performs feature imputation only when cross-view transfer is feasible, avoiding unreliable imputations; 3) an energy-based representation fusion module that aggregates observed and selectively imputed features across views via a view attention mechanism, generating view-coherent representations; 4) an energy-enhanced contrastive alignment module that enforces consistency between view-specific and view-coherent representations using dual-level energy signals to preserve true positives. Extensive experiments demonstrate that Energy-DIMC outperforms state-of-the-art IMVC methods across diverse missing-view scenarios. The code is available at https://github.com/sunway677/EnergyIMVC.

Rights

© 2025 Copyright held by the owner/authors.

This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Wang, Z., Du, Y., Ning, R., & Li, L. (2026). Energy-based deep incomplete multi-view clustering. In MM '25: Proceedings of the 33rd ACM International Conference on Multimedia (pp. 1686-1694). Association for Computing Machinery. https://doi.org/10.1145/3746027.3755365

ORCID

0009-0006-0199-1231 (Wang), 0009-0000-8522-1598 (Du), 0000-0003-4050-6252 (Ning), 0000-0002-4323-2632 (Li)

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