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文章基本信息

  • 标题:Nonlinear Robust Regression Using Kernel Principal Component Analysis and R-Estimators
  • 作者:Antoni Wibowo ; Mohammad Ishak Desa
  • 期刊名称:International Journal of Computer Science Issues
  • 印刷版ISSN:1694-0784
  • 电子版ISSN:1694-0814
  • 出版年度:2011
  • 卷号:8
  • 期号:5
  • 出版社:IJCSI Press
  • 摘要:In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel principal component regression (KPCR). KPCR can be viewed as a non-linearization of principal component regression (PCR) which uses the ordinary least squares (OLS) for estimating its regression coefficients. We use PCR to dispose the negative effects of multicollinearity in regression models. However, it is well known that the main disadvantage of OLS is its sensitiveness to the presence of outliers. Therefore, KPCR can be inappropriate to be used for data set containing outliers. In this paper, we propose a novel nonlinear robust technique using hybridization of KPCA and R-estimators. The proposed technique is compared to KPCR and gives better results than KPCR.
  • 关键词:Kernel principal component analysis; kernel principal component regression; robustness; nonlinear robust regression; R;estimators.
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