172 citations · 664 across the 34 of their papers we have counts for
14 papers · 1 filter
Model-based clustering with Hidden Markov Model regression for time series with regime changes
Faicel Chamroukhi, Allou Samé, Patrice Aknin +1
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governe…
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…
Supervised learning of a regression model based on latent process. Application to the estimation of fuel cell life time
Raïssa Onanena, Faicel Chamroukhi, Latifa Oukhellou +3
This paper describes a pattern recognition approach aiming to estimate fuel cell duration time from electrochemical impedance spectroscopy measurements. It consists in first extrac…
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…
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Dorra Trabelsi, Samer Mohammed, Faicel Chamroukhi +2
Using supervised machine learning approaches to recognize human activities from on-body wearable accelerometers generally requires a large amount of labelled data. When ground trut…
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
Faicel Chamroukhi, Samer Mohammed, Dorra Trabelsi +2
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as he…
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…