Nonlinear component analysis as a kernel eigenvalue problem

被引：7944

作者：

Scholkopf, B ^{[1
]}

Smola, A

Muller, KR

机构：

[1] Max Planck Inst Biol Cybernet, D-72076 Tubingen, Germany

[2] GMD First Forschungszentrum Informat Tech, D-12489 Berlin, Germany

来源：

NEURAL COMPUTATION | 1998年 / 10卷 / 05期

关键词：

D O I：

10.1162/089976698300017467

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map-for instance, the space of all possible five-pixel products in 16 x 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.

引用

页码：1299 / 1319

页数：21

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