Abstract
This paper proposes a new visualization tool based on feature selection and the identification of underlying factors. The goal of this method is to visualize and extract information from complex and high dimensional data sets. The model proposed is an extension of Maximum Likelihood Hebbian Learning based on a family of cost functions, which maximizes the likelihood of identifying a specific distribution in the data while minimizing the effect of outliers. We present and demonstrate a hierarchical extension method which provides an interactive method for visualizing and identifying possibly hidden structure in the dataset. We have applied this method to investigate and visualize the thermal evolution of several frequent construction materials under different thermal and humidity environmental conditions.
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Corchado, E., Burgos, P., del Mar Rodríguez, M., Tricio, V. (2004). A Hierarchical Visualization Tool to Analyse the Thermal Evolution of Construction Materials. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2004. Lecture Notes in Computer Science, vol 3190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30103-5_28
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DOI: https://doi.org/10.1007/978-3-540-30103-5_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23149-3
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