Image Matrix-based Kernel Principal Component Analysis Technique
Xiumei Gao · Journal of Huaiyin Teachers College · 2010
As a novel feature extraction method,kernel principal component analysis technique(KPCA) has been applied to image recognition tasks such as human faces.There are a key problems: whose time complexity depends on the number of the training samples N.When N is very big,time the KPCA consumes is considerable large.This paper proposes image matrix-based kernel principal component analysis technique(I-KPCA) for solution of the above problems in kernel methods.Finally,the experimental results on Concordia University CENPARMI handwriting numeral database indicate that the proposed method is effective and more efficient than KPCA.