A tutorial on Principal Components Analysis

Introduction This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvec-tors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar. There are examples all the way through this tutorial that are meant to illustrate the concepts being discussed. If further information is required, the mathematics textbook " Elementary Linear Algebra 5e " ISBN 0-471-85223-6 is a good source of information regarding the mathematical background .