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June 20, 2026IEEE Transactions on Neural Networks and Learning Systems

Not All, but the Right Ones: Energy-Guided Representation Learning for Incomplete Multiview Clustering

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Authors

ZWZiyu WangJLJian LiYDYiming Du

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Overview

Randomized trial demonstrates improved clustering accuracy in incomplete data, suggesting a method for better data utilization.

Key Points

  • The aim is to propose a selective imputation framework that effectively manages missing data in multiview clustering.
  • Developed ERL-Net, which includes multiview feature extraction with autoencoders.
  • Implemented energy-geometric graph encoding to assess feature reliability.
  • Adopted energy-gated imputation to selectively reconstruct only reliable views.
  • ERL-Net significantly enhances clustering accuracy, particularly in datasets with high missing data.
  • Outperformed state-of-the-art methods across multiple benchmark datasets in various testing scenarios.
  • Demonstrated effective integration of observed and imputed features while maintaining semantic consistency.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a362e1edb0793dc1a535fb3https://doi.org/10.1109/tnnls.2026.3702954
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Also Consider

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  1. 1URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering2024 · 1 citations
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  3. 3Robust Representation Learning for Clean Feature Discovery in Incomplete Multi‐View Clustering2026
  4. 4Incomplete Multi-View Clustering Via Inference and Evaluation2024 · 6 citations
  5. 5Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures2024 · 28 citations