January 2018
Beginner to intermediate
316 pages
7h 14m
English
Content preview from Feature Engineering Made Easy
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How PCA works
A PCA works by invoking a process called the eigenvalue decomposition of the covariance of a matrix. The mathematics behind this was first published in the 1930s and involves a bit of multivariable calculus and linear algebra. For the purposes of this text, we will skip over that and get to the good part.
PCA may also work on the correlation matrix. You may choose to use the correlation matrix if the features are on a similar scale while covariance matrices are more useful when using different scales. We generally recommend using the covariance matrix with scaled data.
This process happens in four steps:
- Create the covariance matrix of the dataset
- Calculate the eigenvalues of the covariance matrix
- Keep the top k eigenvalues ...
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