摘要
稀疏主成分分析是最近才提出来的一种多元统计分析方法,并成功地用来解决若干降维和数据处理问题,论文分析和总结了稀疏主成分的优点,给出了求解各种稀疏主成分的算法,并将各种稀疏主成分分析方法引入综合评价,通过实例说明了稀疏主成分在综合评价应用中的有效性。
The recently referred sparse principal component analysis（S-PCA） is a method of multivariate statistical analysis, which has been used in date processing and dimensionality reduction successfully. In this paper, we point out the advantage of sparse principal component analysis, and give all kinds of algorithms to solve sparse principal component. Finally, we introduce various S-PCA to comprehensive evaluation and explain the efficiency on the basis of examples.
出处
《财经理论与实践》
CSSCI
北大核心
2009年第5期106-109,共4页
The Theory and Practice of Finance and Economics
基金
国家自然科学基金资助项目(10771217)
关键词
稀疏主成分
降维和数据处理
综合评价
Sparse Principal Component, Date Processing and Dimensionality Reduction, Comprehensive Assessment
