| 稀疏主成分在综合评价中的应用 |
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| 引用本文： | 喻胜华,张新波.稀疏主成分在综合评价中的应用[J].财经理论与实践,2009,30(5). |
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| 作者姓名： | 喻胜华 张新波 |
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| 作者单位： | 1. 湖南大学,经济与贸易学院,湖南,长沙,410079
2. 中南大学,数学科学与计算技术学院,湖南,长沙,410075 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 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.
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| 关 键 词： | 稀疏主成分 降维和数据处理 综合评价 |
The Application of Sparse Principal Component Analysis in Comprehensive Assessment |
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| YU Sheng-hua,ZHANG Xin-bo.The Application of Sparse Principal Component Analysis in Comprehensive Assessment[J].The Theory and Practice of Finance and Economics,2009,30(5). |
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| Authors: | YU Sheng-hua ZHANG Xin-bo |
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| Institution: | YU Sheng-hua1,ZHANG Xin-bo2 (1.College of Economics , Trade,Hunan University,Changsha,Hunan 410079,China,2.School of Mathematical Sciences , Computing Technology,Central South University,Hunan 410075,China) |
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| Abstract: | 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. |
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| Keywords: | Sparse Principal Component Date Processing and Dimensionality Reduction Comprehensive Assessment |
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