TI-86的主成分分析论证

2008 IEEE Region 5 Conference Pub Date : 2008-04-17 DOI:10.1109/TPSD.2008.4562756

C. Stuerke

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引用次数: 0

摘要

在尝试进行分类时，我们经常测量各种各样的特征。主成分分析(PCA)可以通过降维和最大化特征空间方差来辅助多变量分析。为了演示，本文展示了寻找改进的特征空间的技术，并展示了如何使用TI-86计算器的本地命令将数据投影到该空间中。

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Demonstration of Principal Component Analysis on TI-86

We often measure a variety of features when attempting to perform classification. Principal component analysis (PCA) can assist the multivariate investigation by reducing dimensionality and by maximizing feature space variance. For demonstration, this paper shows the techniques for finding the improved feature space and it shows how to project data into this space, using the native commands of the TI-86 calculator.

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2008 IEEE Region 5 Conference

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