کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9745492 | 1491573 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 79, Issues 1â2, 28 October 2005, Pages 22-30
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 79, Issues 1â2, 28 October 2005, Pages 22-30
نویسندگان
Kyungpil Kim, Jong-Min Lee, In-Beum Lee,