Kernel principal component analysis 论文

详细信息

发表期刊/会议

International Conference on Neural Information Processing

发表日期

1999-02-08

发表年份

1999

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 highdimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d-pixel products in images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.