Document Type

Conference Paper

Publication Date

2026

DOI

10.1609/aaai.v40i25.39235

Publication Title

Proceedings of the AAAI Conference on Artificial Intelligence

Volume

40

Issue

25

Pages

20941-20949

Conference Name

Fortieth AAAI Conference on Artificial Intelligence, AAAI 2026, January 20-27, 2026, Singapore, Singapore

Abstract

Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using intra-view, intra-cluster statistics; (3) an energy-based semantic alignment module, which promotes intra-cluster compactness by minimizing energy variance around low-energy cluster anchors; and (4) a contrastive assignment alignment module, which enhances cross-view consistency and encourages confident, well-separated cluster predictions. Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.

Rights

© 2024, Association for the Advancement of Artificial Intelligence.

Included in accordance with publisher policy.

Original Publication Citation

Du, Y., Wang, Z., Li, J., Ning, R., & Li, L. (2026). Deep incomplete multi-view clustering via hierarchical imputation and alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 40(25), 20941-20949. https://doi.org/10.1609/aaai.v40i25.39235

ORCID

0009-0000-8522-1598 (Du), 0009-0006-0199-1231 (Wang), 0009-0003-8960-6542 (Li), 0000-0003-4050-6252 (Ning), 0000-0002-4323-2632 (Li)

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