Analysing spectroscopic data using hierarchical cooperative maximum likelihood hebbian learning

Abstract

A novel approach to feature selection is presented in this paper, in which the aim is to visualize and extract information from complex, high dimensional spectroscopic data. The model proposed is a mixture of factor analysis and exploratory projection pursuit based on a family of cost functions proposed by Fyfe and MacDonald which maximizes the likelihood of identifying a specific distribution in the data while minimizing the effect of outliers, It employs cooperative lateral connections derived from the Rectified Gaussian Distribution to enforce a more sparse representation in each weight vector. We also demonstrate a hierarchical extension to this method which provides an interactive method for identifying possibly hidden structure in the dataset.