Overview of Principal Component Analysis
Principal component analysis accounts for the total variance of the observed variables (that is, the variance common to all variables and the variance unique to each variable).
If you want to see the arrangement of points across many correlated variables, you can use principal component analysis to show the most prominent directions of the high-dimensional data. Using principal component analysis reduces the dimensionality of a set of data. Principal components representation is important in visualizing multivariate data by reducing it to graphable dimensions. Principal components is a way to picture the structure of the data as completely as possible by using as few variables as possible.
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