本文主要研究内容
作者（2019）在《An Improved Interval-type Symbol Data Principal Component Analysis》一文中研究指出：For interval sample data, a new principal component analysis method based on empirical correlation matrix is proposed. The method is to treat the interval samples as binary variables obeying the norma
Abstract
For interval sample data, a new principal component analysis method based on empirical correlation matrix is proposed. The method is to treat the interval samples as binary variables obeying the norma
论文参考文献
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论文详细介绍
论文作者分别是来自2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)的，发表于刊物2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)2019-02-27论文，是一篇关于,2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)2019-02-27论文的文章。本文可供学术参考使用，各位学者可以免费参考阅读下载，文章观点不代表本站观点，资料来自2019 International Conference on Modeling, Simulation, Optimization and Numerical Techniques(SMONT 2019)2019-02-27论文网站，若本站收录的文献无意侵犯了您的著作版权，请联系我们删除。
标签：2019 International Conference on Modeling; Simulation; Optimization and Numerical Techniques(SMONT 2019)2019-02-27论文;
：An Improved Interval-type Symbol Data Principal Component Analysis论文
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