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Partially multi-view clustering via re-alignment.

Wenbiao Yan1, Jihua Zhu1, Jinqian Chen2

  • 1School of Software Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Yunnan Key Laboratory of Intelligent Systems and Computing, Kunming, 650500, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 16, 2024
PubMed
Summary
This summary is machine-generated.

Partially Multi-view Clustering via Re-alignment (PMVCR) addresses unaligned instances in multi-view data. This method uses contrastive learning and a novel re-alignment process to improve clustering performance.

Keywords:
Contrastive learningMulti-view clusteringPartial view-aligned multi-view learning

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Multi-view clustering aims to enhance clustering by integrating information from multiple data perspectives.
  • Real-world multi-view data often contains unaligned instances due to temporal or spatial asynchrony.
  • Existing alignment methods using transformation matrices are computationally complex and cumbersome.

Purpose of the Study:

  • To propose a novel method, Partially Multi-view Clustering via Re-alignment (PMVCR), for handling partially unaligned instances in multi-view data.
  • To integrate representation learning and data alignment efficiently without complex matrix computations.
  • To improve the effectiveness and generalization of multi-view clustering on datasets with instance misalignment.

Main Methods:

  • A three-stage training process: coarse-grained alignment using contrastive learning with negative instance pairs, a re-alignment stage matching instances based on view representation similarity, and fine-grained alignment for enhanced discriminative power.
  • Representation learning is integrated with data alignment, avoiding the need for learning complex transformation matrices.
  • Utilizes contrastive learning to effectively learn preliminary view representations and leverage information from unaligned samples.

Main Results:

  • The proposed PMVCR method demonstrates superior performance compared to existing multi-view clustering techniques on several benchmark datasets.
  • Effective leveraging of information between unaligned samples enhances overall model generalization.
  • The re-alignment process successfully aligns instances, improving the quality of clustering results.

Conclusions:

  • PMVCR offers an effective and computationally efficient solution for multi-view clustering with partially unaligned instances.
  • The method's ability to learn from unaligned data and enhance representation discriminability leads to improved clustering outcomes.
  • The proposed approach provides a valuable advancement in multi-view clustering methodologies.