%0 Conference Proceedings
%A Donald MacDonald
%A Emilio Corchado
%A Colin Fyfe
%D 2004
%T Analysing Spectroscopic Data Using Hierarchical Cooperative Maximum Likelihood Hebbian Learning
%P 282--291
%X 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 [12] which maximizes the likelihood of identifying a specific distribution in the data while minimizing the effect of outliers [9,12]. It employs cooperative lateral connections derived from the Rectified Gaussian Distribution [8,14] 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.
%U http://www.springerlink.com/content/m00e0wj5u6fp4u0g
%I Springer Berlin / Heidelberg
%B MICAI 2004
%@ 3-540-21459-3