Abstract
A concept that is closely related to linear regression (preceding chapter) is principal components [15.1]. Linear regression addressed the question of how to fit a curve to one set of data, using a minimum number of factors. By contrast, the principal components problem asks how to fit many sets of data with a minimum number of curves. The problem is now of higher dimensionality. Specifically, can each of the data sets be represented as a weighted sum of a “best” set of curves? Each curve is called a “principal component” of the data sets.
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