NDLI: Kernel Partial Least-Squares Regression

Content Provider	IEEE Xplore Digital Library
Author	Bai Yifeng Xiao Jian Yu Long
Copyright Year	2006
Description	Author affiliation: Southwest Jiaotong Univ., Chengdu (Bai Yifeng; Xiao Jian; Yu Long)
Abstract	A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical process control (MSPC) methods. With PLS, the regression matrix is determined on the basis of a subset of the predictor variables and thus, PLS is able to reduce the number of variables to be considered. In this paper, two kinds of KPLS algorithm for construction of nonlinear regression models in possibly high-dimensional feature spaces are provided. According to the idea of structural risk minimization (SRM), the work described here provides an index of generalization errors. We give the theoretical description of the KPLS algorithms and experimentally compare the algorithms with some existing PLS and KPLS regression models. We will demonstrate that on the data sets employed KPLS achieves better results than PLS and some other KPLS. At the same time, validity of index of generalization errors is proved affirmatively.
Starting Page	1231
Ending Page	1238
File Size	244913
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246832
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Least squares methods Principal component analysis Process control Risk management Chemical processes Input variables Neural networks
Content Type	Text
Resource Type	Article

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