August 2019
Intermediate to advanced
342 pages
9h 35m
English
Content preview from Hands-On Artificial Intelligence for Cybersecurity
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Principal component analysis
In formal terms, PCA consists of selecting the hyperplane of space along which the data (represented by points in the space) are mostly spread out; this translates, in mathematical terms, into the search for the axis in which the variance assumes the maximum value.
The following screenshot depicts the principal components of a dataset:
(Image credits: Wikipedia, at https://en.wikipedia.org/wiki/File:GaussianScatterPCA.svg)
To identify this axis, we will need to calculate the covariance matrix associated with our data, identifying the largest Eigenvectors within the matrix, which correspond to the axes associated ...
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