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Multi-view clustering on unmapped data via constrained non-negative matrix factorization.

Linlin Zong1, Xianchao Zhang1, Xinyue Liu1

  • 1Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, 116620, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 11, 2018
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Summary
This summary is machine-generated.

This study introduces a novel multi-view clustering method for unmapped data, utilizing Non-negative Matrix Factorization (NMF) and inter-view constraints. The algorithm effectively clusters data even without direct mapping between views, improving upon existing methods.

Keywords:
Constrained clusteringConstraint selectionMulti-view clusteringNon-negative matrix factorizationUnmapped data

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Area of Science:

  • Machine Learning
  • Data Mining
  • Computer Science

Background:

  • Traditional multi-view clustering relies on mapped data between views.
  • Unmapped data presents a significant challenge in practical multi-view clustering applications.

Purpose of the Study:

  • To develop a multi-view clustering algorithm for unmapped data using Non-negative Matrix Factorization (NMF).
  • To incorporate inter-view constraints to guide clustering when direct data mapping is unavailable.

Main Methods:

  • The proposed method employs NMF-based clustering within each view.
  • It defines disagreement between views based on indicator vectors and cluster assignments.
  • An active inter-view constraint selection strategy is introduced to optimize constraint querying.

Main Results:

  • The algorithm demonstrates strong performance on unmapped data with minimal constraints.
  • It outperforms baseline methods on partially and completely mapped data.
  • Both random and active constraint selection strategies yield effective results.

Conclusions:

  • The developed algorithm offers a robust solution for multi-view clustering with unmapped data.
  • Inter-view constraints are effective in overcoming the lack of direct data mapping.
  • The approach shows promise for real-world scenarios where data views are not fully aligned.