Nonlinear feature extraction based on centroids and kernel functions

被引：21

作者：

Park, CH ^{[1
]}

Park, H ^{[1
]}

机构：

[1] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA

来源：

PATTERN RECOGNITION | 2004年 / 37卷 / 04期

基金：

美国国家科学基金会;

关键词：

cluster structure; dimension reduction; kernel functions; kernel orthogonal centroid method; linear discriminant analysis; nonlinear feature extraction; pattern classification; support vector machines;

D O I：

10.1016/j.patcog.2003.07.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A nonlinear feature extraction method is presented which can reduce the data dimension down to the number of classes, providing dramatic savings in computational costs. The dimension reducing nonlinear transformation is obtained by implicitly mapping the input data into a feature space using a kernel function, and then finding a linear mapping based on an orthonormal basis of centroids in the feature space that maximally separates the between-class relationship. The experimental results demonstrate that our method is capable of extracting nonlinear features effectively so that competitive performance of classification can be obtained with linear classifiers in the dimension reduced space. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

引用

页码：801 / 810

页数：10

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