Skip to Main content Skip to Navigation
Reports

Slope heuristics for variable selection and clustering via Gaussian mixtures

Cathy Maugis 1 Bertrand Michel 1
1 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
Abstract : Specific Gaussian mixtures are considered to solve simultaneously variable selection and clustering problems. A penalized likelihood criterion is proposed in Maugis and Michel (2008) to choose the number of mixture components and the relevant variable subset. This criterion is depending on unknown constants to be approximated in practical situations. A "slope heuristics" method is proposed and experimented to deal with this practical problem in this context. Numerical experiments on simulated datasets, a curve clustering example and a genomics application highlight the interest of the proposed heuristics.
Complete list of metadata

Cited literature [39 references]  Display  Hide  Download

https://hal.inria.fr/inria-00284620
Contributor : Cathy Maugis <>
Submitted on : Wednesday, June 4, 2008 - 12:59:10 PM
Last modification on : Wednesday, September 16, 2020 - 5:05:14 PM
Long-term archiving on: : Tuesday, September 21, 2010 - 4:36:34 PM

File

RR-6550.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00284620, version 2

Collections

Citation

Cathy Maugis, Bertrand Michel. Slope heuristics for variable selection and clustering via Gaussian mixtures. [Research Report] RR-6550, INRIA. 2008. ⟨inria-00284620v2⟩

Share

Metrics

Record views

344

Files downloads

586