Abstract
Kernel learning is widely used in many areas, and many methods are developed. As a famous kernel learning method, kernel principal component analysis (KPCA) endures two problems in the practical applications. One is that all training samples need to be stored for the computing the kernel matrix during kernel learning. Second is that the kernel and its parameter have the heavy influence on the performance of kernel learning. In order to solve the above problem, we present a novel kernel learning namely sparse data-dependent kernel principal component analysis through reducing the training samples with sparse learning-based least squares support vector machine and adaptive self-optimizing kernel structure according to the input training samples. Experimental results on UCI datasets, ORL and YALE face databases, and Wisconsin Breast Cancer database show that it is feasible to improve KPCA on saving consuming space and optimizing kernel structure.