Sparse meta-Gaussian information bottleneck

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Melani Rey, Volker Roth, Thomas Fuchs ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):910-918, 2014.

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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