50 citations · 65 across the 7 of their papers we have counts for
9 papers
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…
An end-to-end data-driven optimisation framework for constrained trajectories
Florent Dewez, Benjamin Guedj, Arthur Talpaert +1
Many real-world problems require to optimise trajectories under constraints. Classical approaches are based on optimal control methods but require an exact knowledge of the underly…
From industry-wide parameters to aircraft-centric on-flight inference: improving aeronautics performance prediction with machine learning
Florent Dewez, Benjamin Guedj, Vincent Vandewalle
Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly…
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…
Model-based clustering for conditionally correlated categorical data
Matthieu Marbac, Christophe Biernacki, Vincent Vandewalle
An extension of the latent class model is presented for clustering categorical data by relaxing the classical "class conditional independence assumption" of variables. This model c…