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Sparse meta-Gaussian information bottleneck.

Publikationsart Peer-reviewed
Publikationsform Originalbeitrag (peer-reviewed)
Publikationsdatum 2014
Autor/in Rey M{é}lanie, Roth Volker, Fuchs Thomas J,
Projekt Copula Distributions in Machine Learning: Models, Inference and Applications
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Originalbeitrag (peer-reviewed)

Zeitschrift JMLR: Workshop and Conference Proceedings
Volume (Issue) Volume 32
Seite(n) 910 - 918
Titel der Proceedings JMLR: Workshop and Conference Proceedings

Abstract

We present a new sparse compression technique based on the information bottleneck (IB) principle, which takes into account side information. This is achieved by introducing a sparse variant of IB which preserves the information in only a few selected dimensions of the original data through compression. By assuming a Gaussian copula we can capture arbitrary non-Gaussian margins, continuous or discrete. We apply our model to select a sparse number of biomarkers relevant to the evolution of malignant melanoma and show that our sparse selection provides reliable predictors.
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