Principal Component Analysis (PCA) is a multivariate technique that is used to reduce the dimension of a dataset while retaining as much information from the data as possible. The pages of this section will provide a bit of background information on some of the concepts and techniques that are used when performing PCA. Although Prism will perform all of the "heavy lifting" in terms of data processing and computation, understanding the basic principles of the concepts involved can be extremely helpful when interpreting PCA results. If you want to skip the theory and get straight to analyzing data, this section of the guide will give you information on each of the options available for the analysis, and this section will help you understand the results that PCA generates.
Feature selection and feature extraction
Projecting data into lower dimensions
Principal Components are defined using variance
Prinipcal components are orthogonal
Classic methods for selecting PCs