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A robust semi-supervised regressor with correntropy-induced manifold regularization and adaptive graph.

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  • 1School of Computer Science and Technology, Soochow University, 215006 Suzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 22, 2024
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Summary

This study introduces a novel correntropy-induced semi-supervised regression (CSSR) method to address noise in data. CSSR enhances robustness through correntropy-induced manifold regularization and adaptive graph construction, outperforming existing methods.

Keywords:
Adaptive graphCorrentropyManifold regularizationRobustnessSemi-supervised regression

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Vision

Background:

  • Semi-supervised regression tasks are crucial for leveraging limited labeled data.
  • Existing methods often overlook the detrimental impact of noise on performance.
  • Data noise is an inherent challenge in real-world datasets.

Purpose of the Study:

  • To propose a novel correntropy-induced semi-supervised regression (CSSR) method.
  • To mitigate the adverse effects of noise in semi-supervised regression.
  • To enhance the robustness and performance of regression models.

Main Methods:

  • Developed a correntropy-induced manifold regularization (CMR) for graph representation learning.
  • Designed a correntropy-induced adaptive graph (CAG) for robust adjacency matrix construction.
  • Integrated CMR, CAG, and correntropy-induced loss within the CSSR framework, solved iteratively.

Main Results:

  • CSSR demonstrated superior performance across synthetic, benchmark, and image datasets.
  • The method effectively mitigates noise, improving regression accuracy.
  • Theoretical analysis and empirical experiments validated the convergence of CSSR.

Conclusions:

  • CSSR offers an effective and robust solution for semi-supervised regression tasks.
  • The proposed CMR and CAG components significantly contribute to noise resilience.
  • The findings highlight the potential of correntropy-based approaches in handling noisy data.