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Updated: Sep 19, 2025

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Incomplete multi-view clustering via efficient anchor tensor recovery framework.

Jintian Ji1, Songhe Feng1, Jie Huang2

  • 1School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China; Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing Jiaotong University, Beijing, 100044, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 6, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Efficient Anchor Tensor Recovery Framework (EATER) for incomplete multi-view clustering. EATER efficiently recovers missing data and enhances geometric structures, outperforming existing methods.

Keywords:
Geometric structure constraintHigh-order correlationIncomplete multi-view clusteringNon-convex tensor rank

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Tensor-based Incomplete Multi-view Clustering (TIMC) methods leverage cross-view correlations for data recovery but face challenges with computational complexity and suboptimal representations.
  • Existing methods often struggle with large-scale datasets and can be inefficient due to complex geometric structure constraints.
  • The Tensor Nuclear Norm (TNN) commonly used in TIMC can over-penalize essential rank components, hindering optimal tensor representation.

Purpose of the Study:

  • To address the limitations of current TIMC methods, particularly computational complexity and representation quality.
  • To propose a novel framework, the Efficient Anchor Tensor Recovery Framework (EATER), for improved incomplete multi-view clustering.
  • To enhance the recovery of missing data and the preservation of geometric structures within multi-view data.

Main Methods:

  • EATER utilizes a group of anchors to construct a low-rank anchor tensor for efficient data recovery, capturing high-order correlations across views.
  • Anchor Laplacian Regularization (ALR) is employed to strengthen the geometric structure within the learned representation tensor.
  • A tighter Non-convex Tensor Rank (NTR) is adopted instead of TNN for more effective capture of multi-view high-order correlations, coupled with an efficient iterative optimization algorithm.

Main Results:

  • The proposed EATER framework demonstrates significant improvements in handling incomplete multi-view data.
  • Experimental results confirm the efficiency and effectiveness of EATER, showing superior performance compared to state-of-the-art methods.
  • The algorithm is time-economical and exhibits favorable convergence properties.

Conclusions:

  • EATER provides an effective and efficient solution for incomplete multi-view clustering by addressing key limitations of existing tensor-based approaches.
  • The framework successfully balances data recovery, high-order correlation capture, and geometric structure enhancement.
  • The proposed method offers a promising advancement for large-scale multi-view learning tasks.