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Maximum Correntropy Criterion with Distributed Method

Author

Listed:
  • Fan Xie

    (School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China)

  • Ting Hu

    (School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China)

  • Shixu Wang

    (School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China)

  • Baobin Wang

    (School of Mathematics and Statistics, South-Central University for Nationalities, Wuhan 430074, China)

Abstract

The Maximum Correntropy Criterion (MCC) has recently triggered enormous research activities in engineering and machine learning communities since it is robust when faced with heavy-tailed noise or outliers in practice. This work is interested in distributed MCC algorithms, based on a divide-and-conquer strategy, which can deal with big data efficiently. By establishing minmax optimal error bounds, our results show that the averaging output function of this distributed algorithm can achieve comparable convergence rates to the algorithm processing the total data in one single machine.

Suggested Citation

  • Fan Xie & Ting Hu & Shixu Wang & Baobin Wang, 2022. "Maximum Correntropy Criterion with Distributed Method," Mathematics, MDPI, vol. 10(3), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:304-:d:728156
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