Sparse meta-Gaussian information bottleneck

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-rey14, title = {Sparse meta-Gaussian information bottleneck}, author = {Rey, Melani and Roth, Volker and Fuchs, Thomas}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {910--918}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/rey14.pdf}, url = {https://proceedings.mlr.press/v32/rey14.html}, 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.} }
Endnote
%0 Conference Paper %T Sparse meta-Gaussian information bottleneck %A Melani Rey %A Volker Roth %A Thomas Fuchs %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-rey14 %I PMLR %P 910--918 %U https://proceedings.mlr.press/v32/rey14.html %V 32 %N 2 %X 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.
RIS
TY - CPAPER TI - Sparse meta-Gaussian information bottleneck AU - Melani Rey AU - Volker Roth AU - Thomas Fuchs BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-rey14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 910 EP - 918 L1 - http://proceedings.mlr.press/v32/rey14.pdf UR - https://proceedings.mlr.press/v32/rey14.html AB - 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. ER -
APA
Rey, M., Roth, V. & Fuchs, T.. (2014). Sparse meta-Gaussian information bottleneck. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):910-918 Available from https://proceedings.mlr.press/v32/rey14.html.

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