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Python: Real World Machine Learning
book

Python: Real World Machine Learning

by Prateek Joshi, John Hearty, Bastiaan Sjardin, Luca Massaron, Alberto Boschetti
November 2016
Beginner to intermediate
941 pages
21h 55m
English
Packt Publishing
Content preview from Python: Real World Machine Learning

Feature decomposition – PCA

PCA is an algorithm commonly used to decompose the dimensions of an input signal and keep just the principal ones. From a mathematical perspective, PCA performs an orthogonal transformation of the observation matrix, outputting a set of linear uncorrelated variables, named principal components. The output variables form a basis set, where each component is orthonormal to the others. Also, it's possible to rank the output components (in order to use just the principal ones) as the first component is the one containing the largest possible variance of the input dataset, the second is orthogonal to the first (by definition) and contains the largest possible variance of the residual signal, and the third is orthogonal to ...

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Publisher Resources

ISBN: 9781787123212Purchase Link