Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit
This paper presents an extension to the learning rules of the Principal Component Analysis Network which has been derived to be optimal for a specific probability density function.W e note this pdf 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 the whole family of these learning rules can be viewed as methods for performing Exploratory Projection Pursuit.W e show that these methods provide a simple robust method for the identification of structure in remote sensing images.