期刊名称：Neural Information Processing: Letters and Reviews
电子版ISSN：1738-2532
出版年度：2007
卷号：11
期号：1
页码：1-6
出版社：Neural Information Processing
摘要：The previous work in [1] uses a direct method to build sparse kernel learning algorithms. In this paper, our goal is to prove the feasibility of using the direct method for building sparse kernel principal component analysis from the theoretical derivation. Firstly we present a least square support vector machine formulation for kernel principal component analysis algorithm, and then we build sparse kernel principal component analysis using the direct method same to the method mentioned in [4], and finally we prove the feasibility of the algorithm from the mathematical derivation. The computation complexity and memory capacity of the algorithm is analyzed.
关键词：Kernel method, kernel principal component analysis, sparse KPCA
