Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

July 1, 1998, Vol. 10, No. 5, Pages 1299-1319
(doi: 10.1162/089976698300017467)
© 1998 Massachusetts Institute of Technology
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Article PDF (588.53 KB)
Abstract

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 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.