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Robust 3D DNA FISH Using Directly Labeled Probes
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Robust orthogonal NMF with label propagation for image clustering.

Jingjing Liu1, Nian Wu2, Xianchao Xiu3

  • 1School of Microelectronics, Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle, Shanghai University, Shanghai, 200444, China; State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai, 201203, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 23, 2026
PubMed
Summary

Robust orthogonal non-negative matrix factorization (RONMF) enhances image clustering by improving noise resistance and leveraging limited supervised information. This novel non-convex framework offers superior performance and robustness compared to existing methods.

Keywords:
Image clusteringLabel propagationNon-convex optimizationNon-negative matrix factorizationOrthogonal constraint

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Non-negative matrix factorization (NMF) is widely used for image clustering but is sensitive to noise and struggles with limited supervised information.
  • Existing NMF methods often fail to provide robust clustering in real-world noisy datasets.
  • Effective integration of supervised information remains a challenge for traditional NMF approaches.

Purpose of the Study:

  • To propose a novel robust orthogonal non-negative matrix factorization (RONMF) framework.
  • To enhance NMF robustness against noise corruption in image clustering.
  • To effectively incorporate limited supervised information for improved clustering accuracy.

Main Methods:

  • Developed a unified non-convex framework incorporating label propagation and graph Laplacian regularization.
  • Introduced a non-convex structure for reconstruction error measurement and orthogonal constraints on the basis matrix.
  • Employed an alternating direction method of multipliers (ADMM) optimization algorithm with closed-form solutions for subproblems.

Main Results:

  • Experimental evaluations on eight public image datasets demonstrated superior performance of RONMF.
  • The proposed RONMF method outperformed state-of-the-art NMF techniques across various standard metrics.
  • Achieved excellent robustness and improved clustering accuracy, especially in the presence of noise.

Conclusions:

  • The proposed RONMF framework offers a robust and effective solution for image clustering.
  • RONMF successfully addresses the limitations of existing NMF methods regarding noise sensitivity and supervised information utilization.
  • The developed ADMM-based algorithm ensures computational efficiency and practical applicability.