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

ESAIM: Probability and Statistics (2012)

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

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topMaugis, 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 -

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