Information theoretic feature selection for high dimensional metagenomic data

Abstract

Extremely high dimensional data sets are common in genomic classification scenarios, but they are particularly prevalent in metagenomic studies that represent samples as abundances of taxonomic units. Furthermore, the data dimensionality is typically much larger than the number of observations collected for each instance, a phenomenon known as curse of… (More)
DOI: 10.1109/GENSIPS.2012.6507749

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@article{Ditzler2012InformationTF, title={Information theoretic feature selection for high dimensional metagenomic data}, author={Gregory Ditzler and Gail L. Rosen and Robi Polikar}, journal={Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)}, year={2012}, pages={143-146} }