Computation and Store Space Constrained-Based Sparse Kernel Data Analysis

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.