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Related Experiment Video

Updated: Jun 17, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information.

Qun Zheng1, Xihong Yang2, Siwei Wang2

  • 1School of Earth and Space Sciences, CMA-USTC Laboratory of Fengyun Remote Sensing, University of Science and Technology of China, Hefei 230026, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 7, 2024
PubMed
Summary

This study introduces CodingNet, a novel deep contrastive multi-view clustering (DCMVC) method. CodingNet effectively captures both diverse and consistent information from shallow and deep features for improved clustering performance.

Keywords:
Asymmetric networkContrastive learningDiverse and consistentMulti-view clustering

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep contrastive multi-view clustering (DCMVC) is crucial for unsupervised learning, focusing on relationships between data views.
  • Existing DCMVC methods often overlook diverse information in shallow features, concentrating solely on deep feature consistency.

Purpose of the Study:

  • To propose CodingNet, a novel network for simultaneously exploring diverse and consistent information in multi-view data.
  • To address the limitations of current DCMVC algorithms by incorporating shallow feature diversity.

Main Methods:

  • An asymmetric network structure is employed to extract shallow and deep features independently.
  • Shallow feature diversity is enforced by approximating their similarity matrix to a zero matrix.
  • A dual contrastive mechanism ensures consistency at both view-feature and pseudo-label levels for deep features.

Main Results:

  • CodingNet demonstrates superior performance across six benchmark datasets.
  • The proposed method outperforms existing state-of-the-art multi-view clustering algorithms.
  • Validation confirms the efficacy of simultaneously exploring diverse and consistent information.

Conclusions:

  • CodingNet offers a significant advancement in unsupervised multi-view clustering.
  • The simultaneous exploration of diverse and consistent information is key to enhancing DCMVC.
  • The proposed approach provides a better description of multi-view data through its novel feature extraction and contrastive mechanisms.