Principal Component Analysis
Principal component analysis (PCA) is a statistical technique of representing high-dimensional data in a low-dimensional space. PCA is usually used to reduce the dimensionality of data so that the data can be further visualized or analyzed in a low-dimensional space. For example, we may use PCA to represent data records with 100 attribute variables by data records with only 2 or 3 variables. In this chapter, a review of multivariate statistics and matrix algebra is first given to lay the mathematical foundation of PCA. Then, PCA is described and illustrated. A list of software packages that support PCA is provided. Some applications of PCA are given with references.
14.1 Review of Multivariate Statistics ...
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