Supervised Feature Selection via Dependence Estimation

Song, L; Smola, AJ; Gretton, A; Borgwardt, KM; Bedo, J

doi:10.1145/1273496.1273600

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Supervised Feature Selection via Dependence Estimation

MPS-Authors

/persons/resource/persons83946

Gretton, A
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://dl.acm.org/citation.cfm?doid=1273496.1273600
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

ICML-2007-Song-Gretton.pdf
(Any fulltext), 708KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Song, L., Smola, A., Gretton, A., Borgwardt, K., & Bedo, J. (2007). Supervised Feature Selection via Dependence Estimation. In Z. Ghahramani (Ed.), ICML '07: 24th International Conference on Machine Learning (pp. 823-830). New York, NY, USA: ACM Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CD6B-B

Abstract

We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.