Random Fourier Filters Under Maximum Correntropy Criterion

Shiyuan Wang, Lujuan Dang, Badong Chen, Shukai Duan, Lidan Wang, Chi Kong Tse

Research output: Journal article publicationJournal articleAcademic researchpeer-review

54 Citations (Scopus)


Random Fourier adaptive filters (RFAFs) project the original data into a high-dimensional random Fourier feature space (RFFS) such that the network structure of filters is fixed while achieving similar performance with kernel adaptive filters. The commonly used error criterion in RFAFs is the well-known minimum mean-square error (MMSE) criterion, which is optimal only under the Gaussian noise assumption. However, the MMSE criterion suffers from instability and performance deterioration in the presence of non-Gaussian noises. To improve the robustness of RFAFs against large outliers, the maximum correntropy criterion (MCC) is applied to RFFS, generating a novel robust random Fourier filter under maximum correntropy (RFFMC). To further improve the filtering accuracy, a random-batch RFFMC (RB-RFFMC) is also presented. In addition, a theoretical analysis on the convergence characteristics and steady-state excess mean-square error of RFFMC and RB-RFFMC is provided to validate their superior performance. Simulation results illustrate that RFFMC and its extension provide desirable filtering performance from the aspects of filtering accuracy and robustness, especially in the presence of impulsive noises.

Original languageEnglish
Article number8345754
Pages (from-to)3390-3403
Number of pages14
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number10
Publication statusPublished - 1 Oct 2018


  • maximum correntropy
  • random feature space
  • Random Fourier adaptive filters
  • random-batch
  • robustness

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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