http://www.genedenovo.com/news/584.html
https://new.qq.com/omn/20190125/20190125B1238Z.html
https://www.cnblogs.com/xiangshancuizhu/archive/2012/03/15/2397508.html
PCA
它是通過變量的線性組合來解釋一組變量的方差-協方差的結構。PCA是用來降維的。
用PCA的主要原因：
數據壓縮.：PCA通常用來把包含大量信息原始數據壓縮到新的復合變量或者維度的較小的集合，同時損失做少的信息。
Interpretation.
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PCA can be used to discover important features of a large data set. It often reveals relationships that were previously unsuspected, thereby allowing interpretations of the data that may not ordinarily result from examination of the data. PCA is typically used as an intermediate step in data analysis when the number of input variables is otherwise too large to perform useful analysis.
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PCA能夠發現大數據集的重要特征。 它常常發現被隱藏的關系。PCA通常在數據分析是一個中間步驟，當輸入變了對於有用的分析太大的話。
Origin提供PCA下列功能：
- Descriptive Statistics 描述統計
- Correlation Matrix 相關矩陣
- Eigenvalues of the Correlation Matrix相關矩陣的特征值
- Extracted Eigenvectors提取的特征向量
- Scores for each observation每個觀察值的值
- Plots作圖
- Scree Plot
- Loading Plot
- BiPlot