edoc

Sparse meta-Gaussian information bottleneck

Rey, Melani and Roth, Volker and Fuchs, Thomas. (2014) Sparse meta-Gaussian information bottleneck. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014). Red Hook, NY, pp. 910-918.

Full text not available from this repository.

Official URL: http://edoc.unibas.ch/dok/A6329080

Downloads: Statistics Overview

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.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Datenanalyse (Roth)
UniBasel Contributors:Roth, Volker and Rey, Mélanie
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Bibsysno:Link to catalogue
Publisher:Curran
Note:Publication type according to Uni Basel Research Database: Conference paper
Related URLs:
Last Modified:05 Jun 2015 08:53
Deposited On:05 Jun 2015 08:53

Repository Staff Only: item control page