Search Machine Learning Repository: Sparse meta-Gaussian information bottleneck
Authors: Melani Rey, Volker Roth and Thomas Fuchs
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Year: 2014
Pages: 910-918
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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