Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering

• URL: http://arxiv.org/abs/2608.15757v1
• Date: Sun, 16 Aug 2026 14:16:24 GMT
• Title: Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering
• Abstract summary: Incomplete multi-view clustering (IMVC) aims to uncover shared cluster structures from data with partially observed views.<n>We propose a variational framework that explicitly goes beyond this assumption by introducing a learnable cross-view correlation structure.<n>Our method consistently outperforms state-of-the-art approaches across diverse missing-view settings.
• Score: 15.204251218204542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incomplete multi-view clustering (IMVC) aims to uncover shared cluster structures from data with partially observed views. Although recent imputation-free methods based on variational inference demonstrate robustness to missing views, they commonly rely on a conditional independence assumption across views in the posterior aggregation stage, which fails to capture the inherently structured and potentially correlated nature of multi-view data. In this paper, we propose a variational framework that explicitly goes beyond this assumption by introducing a learnable cross-view correlation structure. Specifically, we explicitly model and learn correlations between views by utilizing the covariance structure of posterior estimation errors during aggregation. To facilitate robust and efficient learning, the correlation matrix is parameterized through a normalized Cholesky decomposition, ensuring positive definiteness and enabling the entire model to be trained jointly through a unified variational objective. Extensive experiments on multiple IMVC benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches across diverse missing-view settings while introducing only a negligible number of learnable parameters. These results highlight the effectiveness of adaptive correlation modeling in variational IMVC, demonstrating the need to go beyond the independence assumption in IMVC. The code is available at https://github.com/zmxu196/ACOVA.

Related papers

• SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering [46.33455475152849]
  Partially View-aligned Clustering aims to learn correspondences between misaligned view samples.<n>Our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning.<n>Our method consistently outperforms existing approaches on the PVC problem.
  arXiv  Detail & Related papers  (2025-12-17T12:48:41Z)
• Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence [72.41989962665285]
  Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views.<n>However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views.<n>We design a causal multi-view clustering network, termed CauMVC, to tackle this problem.
  arXiv  Detail & Related papers  (2025-09-19T14:31:40Z)
• Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning [65.75756724642932]
  In incomplete multi-view clustering, missing data induce prototype shifts within views and semantic inconsistencies across views.<n>We propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL)<n>FreeCSL achieves more confident and robust assignments on IMVC task, compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2025-05-16T12:37:10Z)
• Balanced Multi-view Clustering [56.17836963920012]
  Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures.<n>The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information.<n>We propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view.
  arXiv  Detail & Related papers  (2025-01-05T14:42:47Z)
• Self Supervised Correlation-based Permutations for Multi-View Clustering [7.093692674858257]
  We propose an end-to-end deep learning-based multi-view clustering framework for general data types.<n>Our approach involves generating meaningful fused representations using a novel permutation-based canonical correlation objective.
  arXiv  Detail & Related papers  (2024-02-26T08:08:30Z)
• Variational Interpretable Learning from Multi-view Data [2.687817337319978]
  DICCA is designed to disentangle both the shared and view-specific variations for multi-view data. Empirical results on real-world datasets show that our methods are competitive across domains.
  arXiv  Detail & Related papers  (2022-02-28T01:56:44Z)
• Latent Correlation-Based Multiview Learning and Self-Supervision: A Unifying Perspective [41.80156041871873]
  This work puts forth a theory-backed framework for unsupervised multiview learning. Our development starts with proposing a multiview model, where each view is a nonlinear mixture of shared and private components. In addition, the private information in each view can be provably disentangled from the shared using proper regularization design.
  arXiv  Detail & Related papers  (2021-06-14T00:12:36Z)
• Trusted Multi-View Classification [76.73585034192894]
  We propose a novel multi-view classification method, termed trusted multi-view classification. It provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed algorithm jointly utilizes multiple views to promote both classification reliability and robustness.
  arXiv  Detail & Related papers  (2021-02-03T13:30:26Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Variational Inference for Deep Probabilistic Canonical Correlation Analysis [49.36636239154184]
  We propose a deep probabilistic multi-view model that is composed of a linear multi-view layer and deep generative networks as observation models. An efficient variational inference procedure is developed that approximates the posterior distributions of the latent probabilistic multi-view layer. A generalization to models with arbitrary number of views is also proposed.
  arXiv  Detail & Related papers  (2020-03-09T17:51:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.