Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures
DOI:
https://doi.org/10.1609/aaai.v38i14.29548Keywords:
ML: Multi-instance/Multi-view Learning, ML: Clustering, ML: Multimodal Learning, ML: Unsupervised & Self-Supervised LearningAbstract
Incomplete multi-view clustering (IMVC) aims to reveal shared clustering structures within multi-view data, where only partial views of the samples are available. Existing IMVC methods primarily suffer from two issues: 1) Imputation-based methods inevitably introduce inaccurate imputations, which in turn degrade clustering performance; 2) Imputation-free methods are susceptible to unbalanced information among views and fail to fully exploit shared information. To address these issues, we propose a novel method based on variational autoencoders. Specifically, we adopt multiple view-specific encoders to extract information from each view and utilize the Product-of-Experts approach to efficiently aggregate information to obtain the common representation. To enhance the shared information in the common representation, we introduce a coherence objective to mitigate the influence of information imbalance. By incorporating the Mixture-of-Gaussians prior information into the latent representation, our proposed method is able to learn the common representation with clustering-friendly structures. Extensive experiments on four datasets show that our method achieves competitive clustering performance compared with state-of-the-art methods.Downloads
Published
2024-03-24
How to Cite
Xu, G., Wen, J., Liu, C., Hu, B., Liu, Y., Fei, L., & Wang, W. (2024). Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures. Proceedings of the AAAI Conference on Artificial Intelligence, 38(14), 16147-16155. https://doi.org/10.1609/aaai.v38i14.29548
Issue
Section
AAAI Technical Track on Machine Learning V