A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction
SCIE
SCOPUS
- Title
- A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction
- Authors
- Kim, K; Lee, JM; Lee, IB
- Date Issued
- 2005-10-28
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- This paper introduces a novel multivariate regression approach based on kernel partial least squares (KPLS) with orthogonal signal correction (OSC). OSC has been proposed as a data preprocessing method that removes from X information not correlated to Y. KPLS is a promising regression method for tackling nonlinear systems because it can efficiently compute regression coefficients in high-dimensional feature spaces by means of nonlinear kernel functions. Unlike other nonlinear partial least squares (PLS) techniques KPLS does not entail any nonlinear optimization procedures and has a complexity similar to that of linear PLS. In this paper, the prediction performance of the proposed approach (OSC-KPLS) is compared to those of PLS, OSC-PLS and KPLS using three examples. OSC-KPLS effectively simplifies both the structure and interpretation of the resulting regression model and shows superior prediction performance compared to linear PLS. (c) 2005 Elsevier B.V. All rights reserved.
- Keywords
- partial least squares (PLS); kernel partial least squares (KPLS); orthogonal signal correction (OSC); multivariate data analysis; NEAR-INFRARED SPECTRA; PRINCIPAL COMPONENT ANALYSIS; REFLECTANCE SPECTRA; NEURAL NETWORKS; PLS; CALIBRATION; MODEL
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24389
- DOI
- 10.1016/j.chemolab.2005.03.003
- ISSN
- 0169-7439
- Article Type
- Article
- Citation
- CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, vol. 79, no. 1-2, page. 22 - 30, 2005-10-28
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- There are no files associated with this item.
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