75 citations · 109 across the 6 of their papers we have counts for
6 papers
Variable selection in model-based clustering and discriminant analysis with a regularization approach
Gilles Celeux, Cathy Maugis-Rabusseau, Mohammed Sedki
Relevant methods of variable selection have been proposed in model-based clustering and classification. These methods are making use of backward or forward procedures to define the…
Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data
Antoine Godichon-Baggioni, Cathy Maugis-Rabusseau, Andrea Rau
Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e., data made up…
Parameter recovery in two-component contamination mixtures: the strategy
Sébastien Gadat, Jonas Kahn, Clément Marteau +1
In this paper, we consider a parametric density contamination model. We work with a sample of i.i.d. data with a common density, , whe…
Multidimensional two-component Gaussian mixtures detection
Béatrice Laurent, Clément Marteau, Cathy Maugis-Rabusseau
Let be a -dimensional i.i.d sample from a distribution with density . The problem of detection of a two-component mixture is considered. Our aim is to de…
Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering
Gilles Celeux, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau +1
We compare two major approaches to variable selection in clustering: model selection and regularization. Based on previous results, we select the method of Maugis et al. (2009b), w…
Non-asymptotic detection of two-component mixtures with unknown means
Béatrice Laurent, Clément Marteau, Cathy Maugis-Rabusseau
This work is concerned with the detection of a mixture distribution from a -valued sample. Given a sample and an even density , our aim is to detect…