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.
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MacDonald, D., Corchado, E., Fyfe, C. (2004). Analysing Spectroscopic Data Using Hierarchical Cooperative Maximum Likelihood Hebbian Learning. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_29
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DOI: https://doi.org/10.1007/978-3-540-24694-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
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