50 citations · 65 across the 7 of their papers we have counts for
6 papers · 1 filter
Non-parametric Multi-Partitions Clustering
Marie du Roy de Chaumaray, Vincent Vandewalle
In the framework of model-based clustering, a model, called multi-partitions clustering, allowing several latent class variables has been proposed. This model assumes that the dist…
Simultaneous semi-parametric estimation of clustering and regression
Matthieu Marbac, Mohammed Sedki, Christophe Biernacki +1
We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group related variables are available. This problem…
Feature quantization for parsimonious and interpretable predictive models
Adrien Ehrhardt, Christophe Biernacki, Vincent Vandewalle +1
For regulatory and interpretability reasons, logistic regression is still widely used. To improve prediction accuracy and interpretability, a preprocessing step quantizing both con…
A tractable Multi-Partitions Clustering
Matthieu Marbac, Vincent Vandewalle
In the framework of model-based clustering, a model allowing several latent class variables is proposed. This model assumes that the distribution of the observed data can be factor…
Model-based clustering of Gaussian copulas for mixed data
Matthieu Marbac, Christophe Biernacki, Vincent Vandewalle
Clustering task of mixed data is a challenging problem. In a probabilistic framework, the main difficulty is due to a shortage of conventional distributions for such data. In this…
Finite mixture model of conditional dependencies modes to cluster categorical data
Matthieu Marbac, Christophe Biernacki, Vincent Vandewalle
We propose a parsimonious extension of the classical latent class model to cluster categorical data by relaxing the class conditional independence assumption. Under this new mixtur…