Deep Variational Incomplete Multi-View Clustering with Information-Theoretic Guidance

Incomplete multi-view clustering (IMVC) remains a challenging problem, as missing views significantly hinder the learning of comprehensive and consistent representations. Existing imputation-based approaches often rely on features of neighboring samples to perform view imputation, which can lead to high reconstruction errors and limited semantic integration. To address the challenges, we propose a novel framework, Deep Variational Incomplete Multi-View Clustering with Information-Theoretic Guidance (DVIMC-ITG). Our approach employs a deep variational autoencoder (VAE) to learn shared latent representations and addresses view missingness by leveraging the mixture of Wasserstein barycenter, effectively capturing the joint distribution of multiple views in a unified latent space. To preserve cross-view consistency while minimizing redundancy, we impose an information-theoretic constraint on the view-specific representations. We formulate a robust Evidence Lower Bound (ELBO) that guides the optimization process toward more informative representation and improved clustering performance. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art incomplete multi-view clustering methods in both clustering accuracy and robustness.