Maximum Correntropy Criterion with Distributed Method

Fan Xie, Ting Hu, Shixu Wang and Baobin Wang
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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

Mathematics, 2022, vol. 10, issue 3, 1-17

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.

Keywords: correntropy; maximum correntropy criterion; distributed method; robustness; error analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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