172 citations · 664 across the 34 of their papers we have counts for
17 papers · 1 filter
Functional Mixtures-of-Experts
Faïcel Chamroukhi, Nhat Thien Pham, Van Hà Hoang +1
We consider the statistical analysis of heterogeneous data for prediction in situations where the observations include functions, typically time series. We extend the modeling with…
Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
Bao Tuyen Huynh, Thin Nguyen-Van, Faicel Chamroukhi +1
Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predictors, and are widely used for regressi…
Robust mixture of experts modeling using the distribution
Faicel Chamroukhi
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification, and clustering. For regression and cluster analyses of continuous…
Robust mixture of experts modeling using the skew distribution
Faicel Chamroukhi
Mixture of Experts (MoE) is a popular framework in the fields of statistics and machine learning for modeling heterogeneity in data for regression, classification and clustering. M…
Bayesian mixtures of spatial spline regressions
Faicel Chamroukhi
This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model wit…
Non-Normal Mixtures of Experts
Faicel Chamroukhi
Mixture of Experts (MoE) is a popular framework for modeling heterogeneity in data for regression, classification and clustering. For continuous data which we consider here in the…
Unsupervised learning of regression mixture models with unknown number of components
Faicel Chamroukhi
Regression mixture models are widely studied in statistics, machine learning and data analysis. Fitting regression mixtures is challenging and is usually performed by maximum likel…
Robust EM algorithm for model-based curve clustering
Faicel Chamroukhi
Model-based clustering approaches concern the paradigm of exploratory data analysis relying on the finite mixture model to automatically find a latent structure governing observed…
Mixture model-based functional discriminant analysis for curve classification
Faicel Chamroukhi, Hervé Glotin
Statistical approaches for Functional Data Analysis concern the paradigm for which the individuals are functions or curves rather than finite dimensional vectors. In this paper, we…
Functional Mixture Discriminant Analysis with hidden process regression for curve classification
Faicel Chamroukhi, Heré Glotin, Céline Rabouy
We present a new mixture model-based discriminant analysis approach for functional data using a specific hidden process regression model. The approach allows for fitting flexible c…
A regression model with a hidden logistic process for feature extraction from time series
Faicel Chamroukhi, Allou Samé, Gérard Govaert +1
A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic pro…
A regression model with a hidden logistic process for signal parametrization
Faicel Chamroukhi, Allou Samé, Gérard Govaert +1
A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are est…
Time series modeling by a regression approach based on a latent process
Faicel Chamroukhi, Allou Samé, Gérard Govaert +1
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques…
A hidden process regression model for functional data description. Application to curve discrimination
Faicel Chamroukhi, Allou Samé, Gérard Govaert +1
A new approach for functional data description is proposed in this paper. It consists of a regression model with a discrete hidden logistic process which is adapted for modeling cu…
Model-based clustering and segmentation of time series with changes in regime
Allou Samé, Faicel Chamroukhi, Gérard Govaert +1
Mixture model-based clustering, usually applied to multidimensional data, has become a popular approach in many data analysis problems, both for its good statistical properties and…
Model-based functional mixture discriminant analysis with hidden process regression for curve classification
Faicel Chamroukhi, Hervé Glotin, Allou Samé
In this paper, we study the modeling and the classification of functional data presenting regime changes over time. We propose a new model-based functional mixture discriminant ana…
Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation
Faicel Chamroukhi
This paper introduces a novel mixture model-based approach for simultaneous clustering and optimal segmentation of functional data which are curves presenting regime changes. The p…