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Balance guided incomplete multi-view spectral clustering.

Lilei Sun1, Jie Wen2, Chengliang Liu2

  • 1School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, China; Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, 518000, China.

Neural Networks : the Official Journal of the International Neural Network Society
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PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph learning method for incomplete multi-view clustering (IMVC). The approach effectively partitions data with missing views, outperforming existing methods.

Keywords:
Graph clusteringIncomplete multi-view clusteringMissing viewsSubspace learning

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Real-world data often presents as incomplete multi-view datasets, posing challenges for traditional clustering algorithms.
  • Existing multi-view clustering methods are generally not applicable to datasets with missing views, necessitating new approaches.

Purpose of the Study:

  • To propose a novel graph learning-based method for incomplete multi-view clustering (IMVC).
  • To develop a unified framework for learning a common consensus graph and obtaining a clustering indicator matrix from incomplete multi-view data.

Main Methods:

  • A graph learning-based approach is employed to handle incomplete multi-view data.
  • A relaxed spectral clustering model is utilized to generate a stable probability consensus representation.
  • A weighted multi-view learning mechanism is introduced to dynamically balance the contributions of different views.

Main Results:

  • The proposed IMVC method successfully partitions incomplete multi-view data.
  • Experimental results demonstrate superior performance compared to state-of-the-art clustering methods on various incomplete multi-view datasets.

Conclusions:

  • The developed graph learning-based IMVC method is effective in addressing the challenge of clustering incomplete multi-view data.
  • The approach fully exploits the intrinsic information within incomplete multi-view datasets, offering a robust solution.