Mathematics (Jan 2022)

Maximum Correntropy Criterion with Distributed Method

  • Fan Xie,
  • Ting Hu,
  • Shixu Wang,
  • Baobin Wang

DOI
https://doi.org/10.3390/math10030304
Journal volume & issue
Vol. 10, no. 3
p. 304

Abstract

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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.

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