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Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Adaptive-weighted deep multi-view clustering with uniform scale representation.

Rui Chen1, Yongqiang Tang2, Wensheng Zhang1

  • 1College of Information Science and Technology, Hainan University, Haikou, 570208, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 13, 2023
PubMed
Summary

This study introduces Adaptive-weighted deep Multi-view Clustering with Uniform scale representation (AMCU), a novel framework for multi-source information integration. AMCU enhances clustering by adaptively weighting views and ensuring uniform scale representation for improved performance.

Keywords:
Adaptive-weighted learningDeep clusteringMulti-view clusteringUniform scale representation

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Area of Science:

  • Machine Learning
  • Data Mining
  • Artificial Intelligence

Background:

  • Multi-view clustering integrates information from multiple sources.
  • Existing methods often fail to account for varying view importance and representation scale uniformity.
  • This leads to suboptimal performance and unclear physical meaning in clustering.

Purpose of the Study:

  • To propose a novel joint learning framework, Adaptive-weighted deep Multi-view Clustering with Uniform scale representation (AMCU).
  • To address limitations in current multi-view clustering methods regarding view importance and representation scale.
  • To improve the stability and performance of multi-view clustering models.

Main Methods:

  • Introduced an adaptive weighting strategy with simplex constraints for view contribution measurement.
  • Incorporated a novel regularizer to learn latent representations with approximately uniform scale.
  • Developed a joint learning framework (AMCU) for enhanced multi-view clustering.

Main Results:

  • The proposed adaptive weighting strategy provides clear physical meaning for multi-view clustering.
  • The uniform scale regularizer ensures stable model training and reduces sensitivity to individual views.
  • AMCU demonstrated superior performance compared to state-of-the-art single-view and multi-view methods on eight real-world datasets.

Conclusions:

  • AMCU effectively addresses the challenges of unequal view importance and non-uniform representation scales in multi-view clustering.
  • The framework offers a more stable and interpretable approach to multi-view data analysis.
  • The results indicate significant improvements over existing methods, highlighting the efficacy of the proposed strategies.