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Related Experiment Video

Updated: Dec 6, 2025

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Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification.

Liang-Rui Ren1, Ying-Lian Gao2, Jin-Xing Liu3

  • 1School of Computer Science, Qufu Normal University, Rizhao, 276826, China.

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|October 8, 2020
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Summary
This summary is machine-generated.

This study introduces a robust extreme learning machine (ELM) method using correntropy induced loss and graph regularization. The novel CSRGELM approach enhances classification accuracy by mitigating noise and outliers, outperforming existing techniques.

Keywords:
BioinformaticsCorrentropy induced lossExtreme learning machineSupervised learning

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Area of Science:

  • Machine Learning
  • Computational Intelligence

Background:

  • Extreme Learning Machine (ELM) offers high performance and generalization.
  • ELM's susceptibility to noise and outliers limits its practical application.
  • Robustness remains a key challenge in ELM-based methodologies.

Purpose of the Study:

  • To develop a robust ELM variant resistant to noise and outliers.
  • To enhance classification performance through sparsity and graph regularization.
  • To address the non-convex optimization problem in ELM.

Main Methods:

  • Correntropy induced loss (CIL) integration for noise resilience.
  • L2,1-norm regularization for sparse output weights.
  • Graph regularization to preserve data structure.
  • Half quadratic optimization for iterative solution.

Main Results:

  • CSRGELM demonstrates improved robustness against noise and outliers.
  • Sparsity and graph regularization enhance classification accuracy.
  • The proposed iterative method effectively solves the non-convex CSRGELM problem.

Conclusions:

  • CSRGELM achieves superior classification results on benchmark datasets.
  • The method shows promise in real-world applications like cancer sample classification.