10 results on '"POGGI, JEAN-MICHEL"'
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2. Clustering Signals Using Wavelets
- Author
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Misiti, Michel, Misiti, Yves, Oppenheim, Georges, Poggi, Jean-Michel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sandoval, Francisco, editor, Prieto, Alberto, editor, Cabestany, Joan, editor, and Graña, Manuel, editor
- Published
- 2007
- Full Text
- View/download PDF
3. Random Forest-Based Approach for Physiological Functional Variable Selection: Towards Driver's Stress Level Classification
- Author
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El Haouij, Neska, Poggi, Jean-Michel, Ghozi, Raja, Sevestre-Ghalila, Sylvie, Jaïdane, Mériem, CEA-LinkLab, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Telnet Innovation Labs, Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Model selection in statistical learning (SELECT), Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Signaux et Systèmes [Tunis] (UR-U2S-ENIT), Ecole Nationale d'Ingénieurs de Tunis (ENIT), Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM), Université Paris Descartes - Paris 5 (UPD5), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Unité Signaux et Systèmes, Université de Tunis El Manar, 2092, Tunisia.-Ecole Nationale d'Ingénieurs de Tunis ( ENIT ), Telnet Innovation Labs, Laboratoire de Mathématiques d'Orsay ( LM-Orsay ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Model selection in statistical learning ( SELECT ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire de Mathématiques d'Orsay ( LMO ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Université Paris Descartes - Paris 5 ( UPD5 ), Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS ), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Unité Signaux et Systèmes de l'Ecole Nationale d'Ingénieurs de Tunis
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Recursive feature elimination ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,[STAT.ML]Statistics [stat]/Machine Learning [stat.ML] ,[ STAT.AP ] Statistics [stat]/Applications [stat.AP] ,Grouped variable importance ,[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing ,Random forests ,Wavelets ,Physiological signals ,Functional data ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,[ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML] - Abstract
This paper is devoted to a statistical physiological functional variable selection for driver's stress level classification using random forests. Indeed, this study focuses on humans physiological changes, produced when driving in different urban routes, captured using portable sensors. Specifically, the electrodermal activity measured on two different locations: hand and foot, electromyogram, heart rate and respiration of ten driving experiments in three types of routes: rest area, city, and highway driving issued from drivedb database, available online on the PhysioNet website. Several studies were achieved on driver's stress level recognition using physiological signals. Classically, researchers extract expert-based features from physiological signals and select the most relevant ones for stress level recognition. This work provides a random forest-based method for the selection of physiological functional variables in order to classify the driver's stress level. On the methodological side, the contributions of this work are to consider physiological signals as functional variables, decomposed on wavelet basis and to offer a procedure of variable selection. On the applied side, the proposed method provides a " blind " procedure of driver's stress level classification performing as the expert-based study in terms of misclassification rate. It offers moreover a ranking of physiological variables according to their importance in stress level classification. The obtained results suggest that electromyogram and heart rate signals are not very relevant when compared to the electro-dermal and the respiration signals.
- Published
- 2017
4. Multistep Forecasting Non-Stationary Time Series using Wavelets and Kernel Smoothing
- Author
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Aminghafari, Mina, Poggi, Jean-Michel, Amirkabir University of Technology (AUT), Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Model selection in statistical learning (SELECT), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
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Time series ,MathematicsofComputing_NUMERICALANALYSIS ,Kernel smoothing ,[INFO]Computer Science [cs] ,Wavelets ,Forecasting ,Nonstationary - Abstract
International audience; The authors deal with forecasting nonstationary time series using wavelets and kernel smoothing. Starting from a basic forecasting procedure based on the regression of the process on the nondecimated Haar wavelet coefficients of the past, the procedure was extended in various directions, including the use of an arbitrary wavelet or polynomial fitting for extrapolating low-frequency components. The authors study a further generalization of the prediction procedure dealing with multistep forecasting and combining kernel smoothing and wavelets. They finally illustrate the proposed procedure on nonstationary simulated and real data and then compare it to well-known competitors.
- Published
- 2012
5. Classification supervisée en grande dimension. Application à l'agrément de conduite automobile
- Author
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Poggi, Jean-Michel, Tuleau, Christine, Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Model selection in statistical learning (SELECT), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Contrat de recherche entre la direction de la recherche de Renault et le laboratoire de mathématiques d'Orsay, Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)
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[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] ,CART ,Mathematics - Statistics Theory ,[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] ,Wavelets ,Classification - Abstract
This work is motivated by a real work problem: objectivization. It consists in explaining the subjective drivability using physical criteria coming from signals measured during experiments. We suggest an approach for the discriminant variables selection trying to take advantage of the functional nature of the data. The porblem is ill-posed, since the number of explanatory variables is hugely greater than the sample size. The strategy proceeds in three steps: a signal preprocessing including wavelet denoising and synchronization, dimensionality reduction by compression using a common wavelet basis, and finally the selection of useful variables using a stepwise strategy involving successive applications of the CART method.
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- 2006
6. CLUSTERING FUNCTIONAL DATA USING WAVELETS.
- Author
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ANTONIADIS, ANESTIS, BROSSAT, XAVIER, CUGLIARI, JAIRO, and POGGI, JEAN-MICHEL
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ELECTRIC power distribution ,ENERGY consumption ,DATA analysis ,PERFORMANCE evaluation ,FEATURE selection ,ALGORITHMS ,WAVELETS (Mathematics) - Abstract
We present two strategies for detecting patterns and clusters in high-dimensional timedependent functional data. The use on wavelet-based similarity measures, since wavelets are well suited for identifying highly discriminant local time and scale features. The multiresolution aspect of the wavelet transform provides a time-scale decomposition of the signals allowing to visualize and to cluster the functional data into homogeneous groups. For each input function, through its empirical orthogonal wavelet transform the first strategy uses the distribution of energy across scales to generate a representation that can be sufficient to make the signals well distinguishable. Our new similarity measure combined with an efficient feature selection technique in the wavelet domain is then used within more or less classical clustering algorithms to effectively differentiate among high-dimensional populations. The second strategy uses a similarity measure between the whole time-scale representations that is based on wavelet-coherence tools. The clustering is then performed using a k-centroid algorithm starting from these similarities. Practical performance is illustrated through simulations as well as daily profiles of the French electricity power demand [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
7. Nonstationary Time Series Forecasting Using Wavelets and Kernel Smoothing.
- Author
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Aminghafari, Mina and Poggi, Jean-Michel
- Subjects
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TIME series analysis , *WAVELETS (Mathematics) , *KERNEL functions , *POLYNOMIALS , *REGRESSION analysis , *HARMONIC analysis (Mathematics) - Abstract
The authors deal with forecasting nonstationary time series using wavelets and kernel smoothing. Starting from a basic forecasting procedure based on the regression of the process on the nondecimated Haar wavelet coefficients of the past, the procedure was extended in various directions, including the use of an arbitrary wavelet or polynomial fitting for extrapolating low-frequency components. The authors study a further generalization of the prediction procedure dealing with multistep forecasting and combining kernel smoothing and wavelets. They finally illustrate the proposed procedure on nonstationary simulated and real data and then compare it to well-known competitors. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
8. FORECASTING TIME SERIES USING WAVELETS.
- Author
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AMINGHAFARI, MINA and POGGI, JEAN-MICHEL
- Subjects
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WAVELETS (Mathematics) , *HARMONIC analysis (Mathematics) , *FORECASTING , *EQUATIONS , *NUMERICAL solutions to equations - Abstract
This paper deals with wavelets in time series, focusing on statistical forecasting purposes. Recent approaches involve wavelet decompositions in order to handle non-stationary time series in such context. A method, proposed by Renaud et al.,11 estimates directly the prediction equation by direct regression of the process on the Haar non-decimated wavelet coefficients depending on its past values. In this paper, this method is studied and extended in various directions. The new variants are used first for stationary data and after for stationary data contaminated by a deterministic trend. [ABSTRACT FROM AUTHOR]
- Published
- 2007
- Full Text
- View/download PDF
9. Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting.
- Author
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Auder, Benjamin, Cugliari, Jairo, Goude, Yannig, and Poggi, Jean-Michel
- Subjects
SMART power grids ,LOAD forecasting (Electric power systems) ,ENERGY management ,FORECASTING ,DATA analysis - Abstract
Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The first section is dedicated to the industrial context and a review of individual electrical data analysis. Then, we focus on hierarchical time-series for bottom-up forecasting. The idea is to decompose the global signal and obtain disaggregated forecasts in such a way that their sum enhances the prediction. This is done in three steps: identify a rather large number of super-consumers by clustering their energy profiles, generate a hierarchy of nested partitions and choose the one that minimize a prediction criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy gives a 16% improvement in forecasting accuracy when applied to French individual consumers. Then, this strategy is implemented using R—the free software environment for statistical computing—so that it can scale when dealing with massive datasets. The proposed solution is to make the algorithm scalable combine data storage, parallel computing and double clustering step to define the super-consumers. The resulting software is openly available. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. Multivariate denoising using wavelets and principal component analysis
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Aminghafari, Mina, Cheze, Nathalie, and Poggi, Jean-Michel
- Subjects
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MULTIVARIATE analysis , *WAVELETS (Mathematics) , *PRINCIPAL components analysis , *SIGNAL processing - Abstract
Abstract: A multivariate extension of the well known wavelet denoising procedure widely examined for scalar valued signals, is proposed. It combines a straightforward multivariate generalization of a classical one and principal component analysis. This new procedure exhibits promising behavior on classical bench signals and the associated estimator is found to be near minimax in the one-dimensional sense, for Besov balls. The method is finally illustrated by an application to multichannel neural recordings. [Copyright &y& Elsevier]
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- 2006
- Full Text
- View/download PDF
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