Kernel continuum regression

被引:6
|
作者
Lee, Myung Hee [1 ]
Liu, Yufeng [2 ]
机构
[1] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
[2] Univ N Carolina, Carolina Ctr Genome Sci, Dept Biostat, Dept Stat & Operat Res, Chapel Hill, NC 27599 USA
基金
美国国家科学基金会;
关键词
Continuum regression; Kernel regression; Ordinary least squares; Principal component regression; Partial least squares; PARTIAL LEAST-SQUARES; NONLINEAR-REGRESSION; SELECTION; MODELS; VIEW; PCA;
D O I
10.1016/j.csda.2013.06.016
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The continuum regression technique provides an appealing regression framework connecting ordinary least squares, partial least squares and principal component regression in one family. It offers some insight on the underlying regression model for a given application. Moreover, it helps to provide deep understanding of various regression techniques. Despite the useful framework, however, the current development on continuum regression is only for linear regression. In many applications, nonlinear regression is necessary. The extension of continuum regression from linear models to nonlinear models using kernel learning is considered. The proposed kernel continuum regression technique is quite general and can handle very flexible regression model estimation. An efficient algorithm is developed for fast implementation. Numerical examples have demonstrated the usefulness of the proposed technique. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:190 / 201
页数:12
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