Principal component analysis
Computation using the covariance method

The following is a detailed description of PCA using the covariance method as opposed to the correlation method.

The goal is to transform a given data set X of dimension p to an alternative data set Y of smaller dimension L. Equivalently, we are seeking to find the matrix Y, where Y is the Karhunen–Loève transform (KLT) of matrix X:

Y = K L T { X } {\displaystyle \mathbf {Y} =\mathbb {KLT} \{\mathbf {X} \}}
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