Kernel PCA for novelty detection
本文档由 qiao1234567 分享于2010-05-27 15:27
Kernel principal component analysis (kernel PCA) is a non-linear extension of PCA. This study introduces and investigates the use of kernel PCA for novelty detection. Training data are mapped into an infinite-dimensional feature space. In this space, kernel PCA extracts the principal components of the data distribution. The squared distance to the corres..
君,已阅读到文档的结尾了呢~~