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Analysing Spectroscopic Data Using Hierarchical Cooperative Maximum Likelihood Hebbian Learning

  • Donald MacDonald
  • Emilio Corchado
  • Colin Fyfe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2972)

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 [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.

Keywords

Cost Function Energy Function Spectroscopic Data Support Vector Regression Learning Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Donald MacDonald
    • 1
    • 2
  • Emilio Corchado
    • 1
    • 2
  • Colin Fyfe
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosSpain
  2. 2.Applied Computational Intelligence Research UnitUniversity of PaisleyScotland

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