PCA overview 1. pre-processing for empirical modeling, 2. data compression and 3. noise suppression.
|1 Mar 2010||Helen Chen||
The last posting to this Web log, Principal Components Analysis (Feb-26-2010), gave an overview of principal component analysis (PCA), and how to effect it within MATLAB. This article will cover three uses of PCA: 1. pre-processing for empirical modeling, 2. data compression and 3. noise suppression.
To serve the widest possible audience, this article will conduct PCA using only base MATLAB functions, but realize that users with the Statistics Toolbox have, as mentioned in the last posting, the option of using tools like princomp and zscore.
We will continue to use the very small data set used in the last article: