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Updated: Sep 18, 2025

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Scalable one-pass multi-view clustering with tensorized multiscale bipartite graphs fusion.

Fei Wang1, Gui-Fu Lu1

  • 1School of Computer and Information, Anhui Polytechnic University, Wuhu, Anhui, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 24, 2025
PubMed
Summary

This study introduces a novel multi-view clustering method, Scalable One-pass Multi-View Clustering with Tensorized Multiscale Bipartite Graphs Fusion (SOMVC/TMBGF), for efficient large-scale data analysis. SOMVC/TMBGF improves clustering accuracy by fusing multiscale bipartite graphs and dynamically weighting data views.

Keywords:
Bipartite graphLarge-scale datasetMulti-view clustering

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Science

Background:

  • Anchor-based methods are common for large-scale multi-view clustering but often use single-scale graphs, limiting data representation.
  • Existing algorithms may require post-processing and lack dynamic view contribution adjustment.

Purpose of the Study:

  • To develop an advanced multi-view clustering technique for improved accuracy and efficiency in large-scale datasets.
  • To overcome limitations of single-scale graphs and static view weighting in existing methods.

Main Methods:

  • Proposed Scalable One-pass Multi-View Clustering with Tensorized Multiscale Bipartite Graphs Fusion (SOMVC/TMBGF).
  • Generated multiscale bipartite graphs per view and fused them adaptively for partition matrix creation.
  • Employed Tensor Schatten p-norm for tensorized fusion of view-specific partition matrices.
  • Integrated partition matrix learning and clustering with weighted spectral rotation for dynamic view contribution.

Main Results:

  • SOMVC/TMBGF demonstrated superior clustering performance compared to existing methods.
  • The proposed method achieved significant improvements in computational efficiency.
  • Effectively handled large-scale multi-view data by leveraging multiscale structural information.

Conclusions:

  • SOMVC/TMBGF offers a scalable and efficient solution for multi-view clustering.
  • The tensorized multiscale bipartite graph fusion approach enhances data representation and clustering accuracy.
  • Dynamic adjustment of view contributions leads to more robust and adaptive clustering outcomes.