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Machine Learning
book

Machine Learning

by Mohssen Mohammed, Muhammad Badruddin Khan, Eihab Mohammed Bashier
August 2016
Intermediate to advanced content levelIntermediate to advanced
204 pages
3h 51m
English
CRC Press
Content preview from Machine Learning

Chapter 12

Principal Component Analysis

12.1 Introduction

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. This transformation is defined in such a way that the first principal component has the largest possible variance (i.e., accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors ...

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Publisher Resources

ISBN: 9781315354415