Principal Component Analysis (PCA) is a simple yet popular and useful linear transformation technique that is used in numerous applications, such as stock market predictions, the analysis of gene expression data, and many more. In this tutorial, we will see that PCA is not just a “black box”, and we are going to unravel its internals in 3 basic steps.
用于数据处理的模块化工具箱可能会有所帮助: http://mdp-toolkit.sourceforge.net/
我要推荐塞巴斯蒂安·拉什卡的Principal Component Analysis in 3 Simple Steps。
它有一个关于PCA的简短的介绍,有大量图片的例子,利用了pandas,而且它完全是在Python上制作的。
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