Classification and ICA using maximum likelihood Hebbian learning

We investigate an extension of Hebbian learning in a principal component analysis network which has been derived to be optimal for a specific probability density function(PDF). We note that this probability density function is one of a family of PDFs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing exploratory projection pursuit (EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.