Kernel Online Learning Algorithm with Scale Adaptation

Shiyuan Wang, Lujuan Dang, Badong Chen, Chengxiu Ling, Lidan Wang, Shukai Duan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Kernel adaptive filtering is implemented by evaluating the inner product between the kernel function-based vector and the coefficient vector. In this brief, the coefficient vector is decomposed into the direction vector and the scale, which are updated using the steepest descent method and thus generate a novel online learning method, namely kernel online learning algorithm with scale adaptation (KOL-SA). In addition, the convergence of KOL-SA is proved and an upper bound of steady-state mean square error is therefore obtained. Simulation results confirm that the proposed KOL-SA achieves desirable filtering performance from the aspects of the filtering accuracy and stability.

Original languageEnglish
Article number8078251
Pages (from-to)1788-1792
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Issue number11
Publication statusPublished - Nov 2018
Externally publishedYes


  • Kernel online learning
  • coefficient update
  • direction vector
  • scale
  • the steepest descent method

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