Variable Selection for Clustering with Gaussian Mixture Models

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Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “[132] selected relevant features using backward stepwise selection for Gaussian mixture models and an integrated likelihood criterion approximated by the Bayesian information criterion to guide the search for features and to determine the number of clusters.”
Model-based clustering of high-dimensional data: A review (2014, methodology): “B ou ve y ro n & B ru n et – D ra ft V er si on of N ov em b er 12 , 20 12 – U n iv er si té P ar is 1 The identifiability of the SRUW model and the consistency of their variable selection problem has been studied in [57].”
Finite mixture models and model-based clustering (2010, unknown): “This shortcoming was addressed by [26].”
A Framework for Feature Selection in Clustering (2010, background): “A related proposal is made in Maugis, Celeux, and Martin-Magniette (2009).”
Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies (2008, methodology): ““Infinite mixture” models like those of Dorazio and Royle (2003) could also be adapted.”