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 descr iption of PCA, this tutorial first introduces mathematical

concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors

and eigenvalues. This background knowledge is meant to make the PCA section

very straightforward, but can be skipped if the concepts are already familiar.