Name
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. |
Year | Venue | Field |
|---|---|---|
2014 | ICML | Pattern recognition,Computer science,Sparse approximation,Copula (probability theory),Side information,Gaussian,Artificial intelligence,Information bottleneck method,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mélanie Rey | 1 | 0 | 0.34 |
| Volker Roth | 2 | 1142 | 111.35 |
| Thomas J. Fuchs | 3 | 343 | 22.48 |