ORCID Profile
0000-0002-5894-3103
Current Organisations
Université de Caen Normandie
,
IRT SystemX
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Publisher: IEE
Date: 2008
DOI: 10.1049/IC:20080334
Publisher: Institute of Mathematical Statistics
Date: 2022
DOI: 10.1214/22-EJS2057
Publisher: IEEE
Date: 08-2014
Publisher: Springer Science and Business Media LLC
Date: 10-2016
Publisher: Springer Singapore
Date: 2019
Publisher: Elsevier BV
Date: 11-2017
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 07-2011
Publisher: Wiley
Date: 18-01-2019
DOI: 10.1002/WIDM.1298
Abstract: Complex data analysis is a central topic of modern statistics and learning systems which is becoming of broader interest with the increasing prevalence of high‐dimensional data. The challenge is to develop statistical models and autonomous algorithms that are able to discern knowledge from raw data, which can be achieved through clustering techniques, or to make predictions of future data via classification techniques. Latent data models, including mixture model‐based approaches, are among the most popular and successful approaches in both supervised and unsupervised learning. Although being traditional tools in multivariate analysis, they are growing in popularity when considered in the framework of functional data analysis (FDA). FDA is the data analysis paradigm in which each datum is a function, rather than a real vector. In many areas of application, including signal and image processing, functional imaging, bioinformatics, etc., the analyzed data are indeed often available in the form of discretized values of functions, curves, or surfaces. This functional aspect of the data adds additional difficulties when compared to classical multivariate data analysis. We review and present approaches for model‐based clustering and classification of functional data. We present well‐grounded statistical models along with efficient algorithmic tools to address problems regarding the clustering and the classification of these functional data, including their heterogeneity, missing information, and dynamical hidden structures. The presented models and algorithms are illustrated via real‐world functional data analysis problems from several areas of application. This article is categorized under: Fundamental Concepts of Data and Knowledge Data Concepts Algorithmic Development Statistics Technologies Structure Discovery and Clustering
Publisher: Elsevier BV
Date: 11-2019
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 06-2009
Publisher: Springer Science and Business Media LLC
Date: 12-10-2011
Publisher: Elsevier BV
Date: 07-2016
DOI: 10.1016/J.NEUNET.2016.03.002
Abstract: 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 data, MoE usually uses normal experts following the Gaussian distribution. However, for a set of data containing a group or groups of observations with heavy tails or atypical observations, the use of normal experts is unsuitable and can unduly affect the fit of the MoE model. We introduce a robust MoE modeling using the t distribution. The proposed t MoE (TMoE) deals with these issues regarding heavy-tailed and noisy data. We develop a dedicated expectation-maximization (EM) algorithm to estimate the parameters of the proposed model by monotonically maximizing the observed data log-likelihood. We describe how the presented model can be used in prediction and in model-based clustering of regression data. The proposed model is validated on numerical experiments carried out on simulated data, which show the effectiveness and the robustness of the proposed model in terms of modeling non-linear regression functions as well as in model-based clustering. Then, it is applied to the real-world data of tone perception for musical data analysis, and the one of temperature anomalies for the analysis of climate change data. The obtained results show the usefulness of the TMoE model for practical applications.
Publisher: IEEE
Date: 08-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-02-2022
DOI: 10.36227/TECHRXIV.19089995
Abstract: Dual-energy computed tomography (DECT) is an advanced CT scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varying image attenuation at different effective scanning energy levels. In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves. More specifically, we construct functional mixture models that integrate spatial context in mixture weights, with mixture component densities being constructed upon the energy decay curves as functional observations. We design unsupervised clustering algorithms by developing dedicated expectation maximization (EM) algorithms for the maximum likelihood estimation of the model parameters. / To our knowledge, this is the first article to adapt statistical FDA tools and model-based clustering to take advantage of the full spectral information provided by DECT. / We evaluate our methods on 91 head and neck cancer DECT scans. Institutional review board approval was obtained for this study with waiver of informed consent. We compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer. /
Publisher: MDPI AG
Date: 06-12-2022
DOI: 10.3390/DIAGNOSTICS12123072
Abstract: Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation–maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer.
Publisher: Informa UK Limited
Date: 05-11-2015
Publisher: IEEE
Date: 12-2009
Publisher: IEEE
Date: 07-2015
Publisher: Elsevier BV
Date: 03-2010
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 07-2009
DOI: 10.1016/J.NEUNET.2009.06.040
Abstract: 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 may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.
Publisher: Elsevier BV
Date: 07-2013
Publisher: MDPI AG
Date: 11-12-2015
DOI: 10.3390/S151229858
Publisher: Elsevier BV
Date: 11-2013
Publisher: Informa UK Limited
Date: 26-05-2022
Publisher: Springer Science and Business Media LLC
Date: 06-08-2021
DOI: 10.1186/S40488-021-00125-0
Abstract: Mixture of experts (MoE) models are widely applied for conditional probability density estimation problems. We demonstrate the richness of the class of MoE models by proving denseness results in Lebesgue spaces, when inputs and outputs variables are both compactly supported. We further prove an almost uniform convergence result when the input is univariate. Auxiliary lemmas are proved regarding the richness of the soft-max gating function class, and their relationships to the class of Gaussian gating functions.
Publisher: Wiley
Date: 13-02-2018
DOI: 10.1002/WIDM.1246
No related grants have been discovered for Faïcel Chamroukhi.