In order to solve the computation and storage space problems of kernel principal component analysis

which come from the large number of the training samples

this paper presents one-class support vector based sparse kernel principal component analysis (SKPCA).This method can be used in the computation-constrained and space-constrained applications

for example

a small scale hardware platform based image retrieval system

medical assistant diagnosis system

and so on.The method uses the constrained optimization equation to seek the few representative samples

and the few representative samples are used to compute the kernel matrix.The method decreases the computing time and decreases the storage space.So under conditions of the limited training samples

the method is to improve the performance of accuracy and efficiency for hardware computing platform-based image processing.