Principal Components Analysis (PCA) is commonly used in many fields
including feature extraction and data compression.  Independent
Components Analysis (ICA) is a new technique that has been
demonstrated to offer improved performance for problems such as blind
source separation where higher-order statistics of the data are
important.  The goal of this project is to investigate the theoretical
relationship between PCA and ICA, and demonstrate the nature of this
relationship on several classes of problems.