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Robust multi-view subspace clustering based on consensus representation and orthogonal diversity.

Nan Zhao1, Jie Bu1

  • 1State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a robust subspace clustering method (RMSCCO) that leverages consensus representation and orthogonal diversity to improve data analysis across multiple views. RMSCCO effectively utilizes complementary information for enhanced clustering performance.

Keywords:
Consensus representationGrouping-enhanced representationMulti-view subspace clusteringOrthogonal diversity

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Multi-view subspace clustering aims to uncover underlying low-dimensional data structures.
  • Understanding relationships between different data views is crucial for effective clustering.
  • Existing methods may not fully exploit complementary and consistent information across views.

Purpose of the Study:

  • To propose a novel robust subspace clustering approach (RMSCCO) using consensus representation and orthogonal diversity.
  • To enhance the utilization of complementary and consistent information from distinct data views.
  • To improve the diversity and reduce redundancy in subspace representation learning.

Main Methods:

  • Developed a unified framework integrating consensus representation and subspace learning.
  • Introduced a novel orthogonality term to enhance representation diversity.
  • Employed grouping-enhanced representation to preserve local geometric structures.
  • Applied L2,1-norm regularization for improved robustness against noise.
  • Utilized the Alternating Direction Method of Multipliers (ADMM) for optimization.

Main Results:

  • The proposed RMSCCO method demonstrated superior performance on six challenging datasets.
  • The novel orthogonality term effectively improved representation diversity.
  • The consensus representation framework successfully captured inter-view consistency.
  • Robustness against noise was enhanced through L2,1-norm regularization.

Conclusions:

  • RMSCCO offers a powerful and robust approach for multi-view subspace clustering.
  • The integration of consensus representation and orthogonal diversity is effective.
  • The method shows significant potential for various data analysis applications.