Least squares support vector regression filter

Xiaoying Deng*, Yong Luo, Tao Liu, Baojun Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

We combine the training and testing stages of support vector regression into a filtering process. Then we prove that the least squares support vector regression (LS-SVR) based on the translation invariant kernel is a linear time-invariant system. And we find that the common radial basis function kernel-based LS-SVR has properties of lowpass and linear phase filter in the applications to signal processing. By investigation, we find that different parameter selections have great effects on the frequency response of the LS-SVR filter. The simulation experiments for image denoising show that the radial basis function kernel-based LS-SVR filter works better than the adaptive Wiener filtering and wavelet transform-based method.

Original languageEnglish
Title of host publicationProceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Pages730-733
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 3rd International Congress on Image and Signal Processing, CISP 2010 - Yantai, China
Duration: 16 Oct 201018 Oct 2010

Publication series

NameProceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Volume2

Conference

Conference2010 3rd International Congress on Image and Signal Processing, CISP 2010
Country/TerritoryChina
CityYantai
Period16/10/1018/10/10

Keywords

  • Linear time-invariant system
  • Lowpass filter
  • Radial basis function kernel
  • Support vector machine

Fingerprint

Dive into the research topics of 'Least squares support vector regression filter'. Together they form a unique fingerprint.

Cite this