Data-driven penalty calibration: A case study for Gaussian mixture model selection

Cathy Maugis; Bertrand Michel

ESAIM: Probability and Statistics (2012)

  • Volume: 15, page 320-339
  • ISSN: 1292-8100

Abstract

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In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: P&S15 (2011) 41–68] , a penalized likelihood criterion is proposed to select a Gaussian mixture model among a specific model collection. This criterion depends on unknown constants which have to be calibrated in practical situations. A “slope heuristics” method is described and experimented to deal with this practical problem. In a model-based clustering context, the specific form of the considered Gaussian mixtures allows us to detect the noisy variables in order to improve the data clustering and its interpretation. The behavior of our data-driven criterion is highlighted on simulated datasets, a curve clustering example and a genomics application.

How to cite

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Maugis, Cathy, and Michel, Bertrand. "Data-driven penalty calibration: A case study for Gaussian mixture model selection." ESAIM: Probability and Statistics 15 (2012): 320-339. <http://eudml.org/doc/222487>.

@article{Maugis2012,
abstract = { In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: P&S15 (2011) 41–68] , a penalized likelihood criterion is proposed to select a Gaussian mixture model among a specific model collection. This criterion depends on unknown constants which have to be calibrated in practical situations. A “slope heuristics” method is described and experimented to deal with this practical problem. In a model-based clustering context, the specific form of the considered Gaussian mixtures allows us to detect the noisy variables in order to improve the data clustering and its interpretation. The behavior of our data-driven criterion is highlighted on simulated datasets, a curve clustering example and a genomics application. },
author = {Maugis, Cathy, Michel, Bertrand},
journal = {ESAIM: Probability and Statistics},
keywords = {Slope heuristics; Penalized likelihood criterion; Model-based clustering; noisy variable detection; slope heuristics; penalized likelihood criterion; model-based clustering},
language = {eng},
month = {1},
pages = {320-339},
publisher = {EDP Sciences},
title = {Data-driven penalty calibration: A case study for Gaussian mixture model selection},
url = {http://eudml.org/doc/222487},
volume = {15},
year = {2012},
}

TY - JOUR
AU - Maugis, Cathy
AU - Michel, Bertrand
TI - Data-driven penalty calibration: A case study for Gaussian mixture model selection
JO - ESAIM: Probability and Statistics
DA - 2012/1//
PB - EDP Sciences
VL - 15
SP - 320
EP - 339
AB - In the companion paper [C. Maugis and B. Michel, A non asymptotic penalized criterion for Gaussian mixture model selection. ESAIM: P&S15 (2011) 41–68] , a penalized likelihood criterion is proposed to select a Gaussian mixture model among a specific model collection. This criterion depends on unknown constants which have to be calibrated in practical situations. A “slope heuristics” method is described and experimented to deal with this practical problem. In a model-based clustering context, the specific form of the considered Gaussian mixtures allows us to detect the noisy variables in order to improve the data clustering and its interpretation. The behavior of our data-driven criterion is highlighted on simulated datasets, a curve clustering example and a genomics application.
LA - eng
KW - Slope heuristics; Penalized likelihood criterion; Model-based clustering; noisy variable detection; slope heuristics; penalized likelihood criterion; model-based clustering
UR - http://eudml.org/doc/222487
ER -

References

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