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Related Experiment Video

Updated: Jun 8, 2025

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Deep Incomplete Multi-view Clustering via Multi-level Imputation and Contrastive Alignment.

Ziyu Wang1, Yiming Du1, Yao Wang1

  • 1Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|November 8, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Multi-level Imputation and Contrastive Alignment (MICA) for deep incomplete multi-view clustering. MICA improves data imputation and clustering accuracy by using multi-level imputation and contrastive alignment, outperforming existing methods.

Keywords:
Contrastive alignmentMulti-level imputationReliable viewsSemantic consistencyTopological structures

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Deep incomplete multi-view clustering (DIMVC) methods often struggle with missing data and low-quality views.
  • Existing imputation strategies may fail due to assumptions of complete data and single-level imputation.

Purpose of the Study:

  • To propose a novel approach, Multi-level Imputation and Contrastive Alignment (MICA), for enhanced DIMVC.
  • To simultaneously improve imputation quality and clustering performance in incomplete multi-view settings.

Main Methods:

  • MICA utilizes individual deep models per view for unified feature learning and cluster assignment.
  • It employs an adaptive cross-view graph for view selection and performs multi-level imputation (feature, data, reconstruction).
  • Contrastive alignment at instance and cluster levels enhances semantic consistency across views.

Main Results:

  • MICA demonstrates superior performance compared to existing DIMVC methods.
  • The multi-level imputation effectively preserves topological structures and ensures accurate feature inference.
  • Contrastive alignment boosts discriminative cluster assignment and semantic consistency.

Conclusions:

  • MICA offers an effective solution for deep incomplete multi-view clustering by addressing limitations in imputation and view quality.
  • The proposed method achieves state-of-the-art results, highlighting the benefits of multi-level imputation and contrastive alignment.