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Robust Unsupervised Feature Selection Algorithm Based on Fuzzy Anchor Graph.

Zhouqing Yan1, Ziping Ma1,2, Jinlin Ma3

  • 1School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China.

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|August 28, 2025
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Summary
This summary is machine-generated.

A new fuzzy anchor graph algorithm (FWFGFS) enhances unsupervised feature selection by incorporating fuzzy data information. This method improves clustering accuracy and reduces noise impact for better feature subset selection.

Keywords:
fuzzy graphfuzzy weightingorthogonal tri-factorizationunsupervised feature selection

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Area of Science:

  • Machine Learning
  • Data Mining
  • Pattern Recognition

Background:

  • Unsupervised feature selection identifies optimal feature subsets without labels.
  • Existing methods struggle with fuzzy data information and noise, impacting cluster structure modeling.
  • Squared error in reconstruction exacerbates noise sensitivity in current approaches.

Purpose of the Study:

  • Propose a robust unsupervised feature selection algorithm, FWFGFS, using fuzzy anchor graphs.
  • Address limitations of existing methods by effectively modeling fuzzy cluster structures and mitigating noise.
  • Enhance the accuracy and robustness of feature selection in unlabeled data.

Main Methods:

  • Develop a fuzzy anchor graph learning mechanism with fuzzy membership distributions for soft cluster assignments.
  • Introduce an adaptive fuzzy weighting mechanism to reduce noise and errors from redundant features.
  • Apply orthogonal tri-factorization to the low-dimensional representation for independent cluster centers.

Main Results:

  • FWFGFS effectively models fuzzy neighborhood relationships, improving clustering accuracy.
  • The adaptive weighting mechanism reduces noise interference in feature selection.
  • Experimental results demonstrate significant improvements in average clustering accuracy (5.68%–13.79%) over state-of-the-art methods on 12 datasets.

Conclusions:

  • FWFGFS offers a robust and accurate approach to unsupervised feature selection by leveraging fuzzy information.
  • The proposed mechanisms enhance cluster structure modeling and noise resilience.
  • FWFGFS represents a significant advancement in feature selection for unlabeled data analysis.