Origin PCA

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
它是通过变量的线性组合来解释一组变量的方差-协方差的结构。PCA是用来降维的。

用PCA的主要原因：

数据压缩.：PCA通常用来把包含大量信息原始数据压缩到新的复合变量或者维度的较小的集合，同时损失做少的信息。

Interpretation. 

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.

PCA能够发现大数据集的重要特征。 它常常发现被隐藏的关系。PCA通常在数据分析是一个中间步骤，当输入变了对于有用的分析太大的话。

Origin提供PCA下列功能： 

• Descriptive Statistics 描述统计
• Correlation Matrix 相关矩阵
• Eigenvalues of the Correlation Matrix相关矩阵的特征值
• Extracted Eigenvectors提取的特征向量
• Scores for each observation每个观察值的值
• Plots作图
• Scree Plot
• Loading Plot
• BiPlot