Visualizing dependencies of spectral features using mutual information

Andrej Gisbrecht, Yoan Miche, Barbara Hammer, Amaury Lendasse

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    The curse of dimensionality leads to problems in machine learning when dealing with high dimensionality. This aspect is particularly pronounced if intrinsically infinite dimensionality is faced such as present for spectral or functional data. Feature selection constitutes one possibility to deal with this problem. Often, it relies on mutual information as an evaluation tool for the feature importance, however, it might be overlaid by intrinsic biases such as a high correlation of neighbored function values for functional data. In this paper we propose to assess feature correlations of spectral data by an overlay of prior dependencies due to the functional nature and its similarity as measured by mutual information, enabling a quick overall assessment of the relationships between features. By integrating the Nyström approximation technique, the usually time consuming step to compute all pairwise mutual informations can be reduced to only linear complexity in the number of features.

    Original languageEnglish
    Title of host publicationESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
    Pages573-578
    Number of pages6
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
    Duration: 24 Apr 201326 Apr 2013
    Conference number: 21

    Conference

    ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
    Abbreviated titleESANN
    CountryBelgium
    CityBruges
    Period24/04/201326/04/2013

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