Graphical introduction to principal components analysis

Principal components analysis (PCA) is probably the most widely used and best known chemometric (or indeed multivariate) technique. A few, out of many, representative articles provide more in-depth discussion for interested readers.1–4 We have published more than 30 articles for more than 5 years in the current series, but without yet mentioning PCA. The next articles will introduce this technique from a variety of viewpoints.

[1]  Carlo Tomasi,et al.  Singular Value Decomposition , 2021, Encyclopedia of Social Network Analysis and Mining.

[2]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[3]  Hui Xiong,et al.  Euclidean Distance , 2008, Encyclopedia of GIS.

[4]  J. A. López del Val,et al.  Principal Components Analysis , 2018, Applied Univariate, Bivariate, and Multivariate Statistics Using Python.

[5]  H. Hotelling Analysis of a complex of statistical variables into principal components. , 1933 .