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Статті в журналах з теми "Réduction de dimension (Statistique)"
Juan, Salvador. "Sur la production sociologique des types et classes de propriétés de la vie quotidienne." Sociétés contemporaines 17, no. 1 (July 1, 1994): 119–40. http://dx.doi.org/10.3917/soco.p1994.17n1.0119.
Повний текст джерелаBontron, Jean-Claude. "La dimension statistique de la ruralité." Pour 228, no. 4 (2015): 57. http://dx.doi.org/10.3917/pour.228.0057.
Повний текст джерелаMorin, Denis. "Allométrie du système urbain du Québec (1941-1971)." Cahiers de géographie du Québec 19, no. 46 (April 12, 2005): 17–37. http://dx.doi.org/10.7202/021246ar.
Повний текст джерелаLe Gorrec, Yann, Hui Peng, Laurent Lefèvre, Boussad Hamroun, and Françoise Couenne. "Systèmes hamiltoniens à ports de dimension infinie. Réduction et propriétés spectrales." Journal Européen des Systèmes Automatisés 45, no. 7-10 (December 30, 2011): 645–64. http://dx.doi.org/10.3166/jesa.45.645-664.
Повний текст джерелаHannequin, P., P. D. Buffaz, C. Giuliani, and R. Zsigmond. "Application clinique osseuse et myocardique d’une méthode originale de réduction du bruit statistique dans les images scintigraphiques." Médecine Nucléaire 40, no. 3 (May 2016): 185. http://dx.doi.org/10.1016/j.mednuc.2016.03.042.
Повний текст джерелаBenoist, Olivier. "Exposé Bourbaki 1158 : Réduction stable en dimension supérieure (d'après Kollár, Hacon-Xu, ...)." Astérisque 422 (2020): 291–326. http://dx.doi.org/10.24033/ast.1137.
Повний текст джерелаChouaf, Seloua, and Youcef Smara. "Méthode de sélection des bandes à base de l'Analyse en Composantes Indépendantes appliquée aux images hyperspectrales de télédétection." Revue Française de Photogrammétrie et de Télédétection, no. 204 (April 8, 2014): 57–62. http://dx.doi.org/10.52638/rfpt.2013.22.
Повний текст джерелаDelay, Frédérick, and Philippe Ackerer. "The reduction of hydrological models for less tedious practical applications." Annales de la Société Géologique du Nord, no. 22 (December 1, 2015): 29–40. http://dx.doi.org/10.54563/asgn.1018.
Повний текст джерелаL’Her, Jean-François, Cécile Le Moigne, and Patrick Savaria. "Importance relative des effets pays et secteurs dans les marchés développés*." Articles 83, no. 2 (February 4, 2008): 201–26. http://dx.doi.org/10.7202/017517ar.
Повний текст джерелаFeillet, Raymonde, Stéphane Héas, and Dominique Bodin. "Corps et identité au grand âge." Le dossier : Les personnes âgées : repenser la vieillesse, renouveler les pratiques 24, no. 1 (February 29, 2012): 21–35. http://dx.doi.org/10.7202/1008216ar.
Повний текст джерелаДисертації з теми "Réduction de dimension (Statistique)"
Girard, Robin. "Réduction de dimension en statistique et application en imagerie hyper-spectrale." Phd thesis, Grenoble 1, 2008. http://www.theses.fr/2008GRE10074.
Повний текст джерелаThis thesis deals with high dimensional statistical analysis. We focus on three different problems motivated by medical applications : curve classification, pixel classification and clustering in hyperspectral images. Our approaches are deeply linked with statistical testing procedures (multiple testing, minimax testing, robust testing, and functional testing) and learning theory. Both are introduced in the first part of this thesis. The second part focuses on classification of High dimensional Gaussian data. Our approach is based on a dimensionality reduction, and we show practical and theorical results. In the third and last part of this thesis we focus on hyperspectral image segmentation. We first propose a pixel classification algorithm based on multi-scale analysis, penalised maximum likelihood and feature selection. We give theorical results and simulations for this algorithm. We then propose a pixel clustering algorithm. It involves wavelet decomposition of observations in each pixel, smoothing with a growing region algorithm and frontier extraction based on a voting scheme
Girard, Robin. "Réduction de dimension en statistique et application en imagerie hyper-spectrale." Phd thesis, Université Joseph Fourier (Grenoble), 2008. http://tel.archives-ouvertes.fr/tel-00379179.
Повний текст джерелаKuentz, Vanessa. "Contributions à la réduction de dimension." Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13871/document.
Повний текст джерелаThis thesis concentrates on dimension reduction approaches, that seek for lower dimensional subspaces minimizing the lost of statistical information. First we focus on multivariate analysis for categorical data. The rotation problem in Multiple Correspondence Analysis (MCA) is treated. We give the analytic expression of the optimal angle of planar rotation for the chosen criterion. If more than two principal components are to be retained, this planar solution is used in a practical algorithm applying successive pairwise planar rotations. Different algorithms for the clustering of categorical variables are also proposed to maximize a given partitioning criterion based on correlation ratios. A real data application highlights the benefits of using rotation in MCA and provides an empirical comparison of the proposed algorithms for categorical variable clustering. Then we study the semiparametric regression method SIR (Sliced Inverse Regression). We propose an extension based on the partitioning of the predictor space that can be used when the crucial linearity condition of the predictor is not verified. We also introduce bagging versions of SIR to improve the estimation of the basis of the dimension reduction subspace. Asymptotic properties of the estimators are obtained and a simulation study shows the good numerical behaviour of the proposed methods. Finally applied multivariate data analysis on various areas is described
Noyel, Guillaume. "Filtrage, réduction de dimension, classification et segmentation morphologique hyperspectrale." Phd thesis, École Nationale Supérieure des Mines de Paris, 2008. http://pastel.archives-ouvertes.fr/pastel-00004473.
Повний текст джерелаLopez, Olivier. "Réduction de dimension en présence de données censurées." Phd thesis, Rennes 1, 2007. http://tel.archives-ouvertes.fr/tel-00195261.
Повний текст джерелаvariable explicative. Nous développons une nouvelle approche de réduction de la dimension afin de résoudre ce problème.
Pedersen, Morten Akhøj. "Méthodes riemanniennes et sous-riemanniennes pour la réduction de dimension." Electronic Thesis or Diss., Université Côte d'Azur, 2023. http://www.theses.fr/2023COAZ4087.
Повний текст джерелаIn this thesis, we propose new methods for dimension reduction based on differential geometry, that is, finding a representation of a set of observations in a space of lower dimension than the original data space. Methods for dimension reduction form a cornerstone of statistics, and thus have a very wide range of applications. For instance, a lower dimensional representation of a data set allows visualization and is often necessary for subsequent statistical analyses. In ordinary Euclidean statistics, the data belong to a vector space and the lower dimensional space might be a linear subspace or a non-linear submanifold approximating the observations. The study of such smooth manifolds, differential geometry, naturally plays an important role in this last case, or when the data space is itself a known manifold. Methods for analysing this type of data form the field of geometric statistics. In this setting, the approximating space found by dimension reduction is naturally a submanifold of the given manifold. The starting point of this thesis is geometric statistics for observations belonging to a known Riemannian manifold, but parts of our work form a contribution even in the case of data belonging to Euclidean space, mathbb{R}^d.An important example of manifold valued data is shapes, in our case discrete or continuous curves or surfaces. In evolutionary biology, researchers are interested in studying reasons for and implications of morphological differences between species. Shape is one way to formalize morphology. This application motivates the first main contribution of the thesis. We generalize a dimension reduction method used in evolutionary biology, phylogenetic principal component analysis (P-PCA), to work for data on a Riemannian manifold - so that it can be applied to shape data. P-PCA is a version of PCA for observations that are assumed to be leaf nodes of a phylogenetic tree. From a statistical point of view, the important property of such data is that the observations (leaf node values) are not necessarily independent. We define and estimate intrinsic weighted means and covariances on a manifold which takes the dependency of the observations into account. We then define phylogenetic PCA on a manifold to be the eigendecomposition of the weighted covariance in the tangent space of the weighted mean. We show that the mean estimator that is currently used in evolutionary biology for studying morphology corresponds to taking only a single step of our Riemannian gradient descent algorithm for the intrinsic mean, when the observations are represented in Kendall's shape space. Our second main contribution is a non-parametric method for dimension reduction that can be used for approximating a set of observations based on a very flexible class of submanifolds. This method is novel even in the case of Euclidean data. The method works by constructing a subbundle of the tangent bundle on the data manifold via local PCA. We call this subbundle the principal subbundle. We then observe that this subbundle induces a sub-Riemannian structure and we show that the resulting sub-Riemannian geodesics with respect to this structure stay close to the set of observations. Moreover, we show that sub-Riemannian geodesics starting from a given point locally generate a submanifold which is radially aligned with the estimated subbundle, even for non-integrable subbundles. Non-integrability is likely to occur when the subbundle is estimated from noisy data, and our method demonstrates that sub-Riemannian geometry is a natural framework for dealing which such problems. Numerical experiments illustrate the power of our framework by showing that we can achieve impressively large range reconstructions even in the presence of quite high levels of noise
I denne afhandling præsenteres nye metoder til dimensionsreduktion, baseret p˚adifferential geometri. Det vil sige metoder til at finde en repræsentation af et datasæti et rum af lavere dimension end det opringelige rum. S˚adanne metoder spiller enhelt central rolle i statistik, og har et meget bredt anvendelsesomr˚ade. En laveredimensionalrepræsentation af et datasæt tillader visualisering og er ofte nødvendigtfor efterfølgende statistisk analyse. I traditionel, Euklidisk statistik ligger observationernei et vektor rum, og det lavere-dimensionale rum kan være et lineært underrumeller en ikke-lineær undermangfoldighed som approksimerer observationerne.Studiet af s˚adanne glatte mangfoldigheder, differential geometri, spiller en vigtig rollei sidstnævnte tilfælde, eller hvis rummet hvori observationerne ligger i sig selv er enmangfoldighed. Metoder til at analysere observationer p˚a en mangfoldighed udgørfeltet geometrisk statistik. I denne kontekst er det approksimerende rum, fundetvia dimensionsreduktion, naturligt en submangfoldighed af den givne mangfoldighed.Udgangspunktet for denne afhandling er geometrisk statistik for observationer p˚a ena priori kendt Riemannsk mangfoldighed, men dele af vores arbejde udgør et bidragselv i tilfældet med observationer i Euklidisk rum, Rd.Et vigtigt eksempel p˚a data p˚a en mangfoldighed er former, i vores tilfældediskrete kurver eller overflader. I evolutionsbiologi er forskere interesseret i at studeregrunde til og implikationer af morfologiske forskelle mellem arter. Former er ´en m˚adeat formalisere morfologi p˚a. Denne anvendelse motiverer det første hovedbidrag idenne afhandling. We generaliserer en metode til dimensionsreduktion brugt i evolutionsbiologi,phylogenetisk principal component analysis (P-PCA), til at virke for datap˚a en Riemannsk mangfoldighed - s˚a den kan anvendes til observationer af former. PPCAer en version af PCA for observationer som antages at være de yderste knuder iet phylogenetisk træ. Fra et statistisk synspunkt er den vigtige egenskab ved s˚adanneobservationer at de ikke nødvendigvis er uafhængige. We definerer og estimerer intrinsiskevægtede middelværdier og kovarianser p˚a en mangfoldighed, som tager højde fors˚adanne observationers afhængighed. Vi definerer derefter phylogenetisk PCA p˚a enmangfoldighed som egendekomposition af den vægtede kovarians i tanget-rummet tilden vægtede middelværdi. Vi viser at estimatoren af middelværdien som pt. bruges ievolutionsbiologi til at studere morfologi svarer til at tage kun et enkelt skridt af voresRiemannske gradient descent algoritme for den intrinsiske middelværdi, n˚ar formernerepræsenteres i Kendall´s form-mangfoldighed.Vores andet hovedbidrag er en ikke-parametrisk metode til dimensionsreduktionsom kan bruges til at approksimere et data sæt baseret p˚a en meget flexibel klasse afsubmangfoldigheder. Denne metode er ny ogs˚a i tilfældet med Euklidisk data. Metodenvirker ved at konstruere et under-bundt af tangentbundet p˚a datamangfoldighedenM via lokale PCA´er. Vi kalder dette underbundt principal underbundtet. Viobserverer at dette underbundt inducerer en sub-Riemannsk struktur p˚a M og vi viserat sub-Riemannske geodæter fra et givent punkt lokalt genererer en submangfoldighedsom radialt flugter med det estimerede subbundt, selv for ikke-integrable subbundter.Ved støjfyldt data forekommer ikke-integrabilitet med stor sandsynlighed, og voresmetode demonstrerer at sub-Riemannsk geometri er en naturlig tilgang til at h˚andteredette. Numeriske eksperimenter illustrerer styrkerne ved metoden ved at vise at denopn˚ar rekonstruktioner over store afstande, selv under høje niveauer af støj
Damon, Cécilia. "Réduction de dimension et régularisation pour l'apprentissage statistique et la prédiction individuelle en IRMf." Paris 11, 2010. http://www.theses.fr/2010PA112107.
Повний текст джерелаPredictive multivariate methods have yet been rarely explored in fMRI at the inter-subject level. An important inter-subjects anatomo-functional variability and the large dimension of fMRI data in comparison to the few number of subjects complicates the identification of the inter-subjects functional variability specific to a phenotype of interest and increases the overfitting phenomenon of classification techniques. Our first objective aims to explore the various approaches available in the field of supervised statistical learning and well-known to control the overfitting problem and more specifically two means: the feature selection and the regularised classification. Our second goal consist in defining a rigorous methodology of the different proposed strategies at several levels: (i) global: comparison of all the strategies based on all the datasets; (ii) local: comparison restricted to a particular subset of strategies based on all the datasets; (iii) individual: comparison of a pair of strategies based on a single dataset. We tested four couples of data (fMRI contrast, phenotypic information) extracted from a large database, including about 200 healthy subjects that have realized the same experimental protocol. We also constructed simulated datasets with a multivariate discriminant signal. The comparative analysis and the function patterns visualisation revealed the strategy combining the multivariate features selection RFE and the SRDA classifier as the most efficient. This strategy identified parcimonious predictive patterns and obtained good predictive performances proved to be relevant only when the contrast-to-noise ratio was strong
Tournier, Maxime. "Réduction de dimension pour l'animation de personnages." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00650696.
Повний текст джерелаZapien, Durand-Viel Karina. "Algorithme de chemin de régularisation pour l'apprentissage statistique." Phd thesis, INSA de Rouen, 2009. http://tel.archives-ouvertes.fr/tel-00557888.
Повний текст джерелаJanon, Alexandre. "Analyse de sensibilité et réduction de dimension. Application à l'océanographie." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00757101.
Повний текст джерелаКниги з теми "Réduction de dimension (Statistique)"
Camara, Diatta. Données pour les politiques et programmes de développement: Besoins d'information statistique dans les Documents de stratégie de réduction de la pauvreté : Atelier régional de formation sur les nouveaux outils d'analyse des données socio-démographiqes issues d'opérations de collecte de grande envergure. Dakar Ponty, Sénégal: UNPFA/Country Technical Services Team Dakar, 2005.
Знайти повний текст джерелаBolla, Marianna, and Tamás Szabados. Multidimensional Stationary Time Series: Dimension Reduction and Prediction. Taylor & Francis Group, 2021.
Знайти повний текст джерелаBolla, Marianna, and Tamás Szabados. Multidimensional Stationary Time Series: Dimension Reduction and Prediction. Taylor & Francis Group, 2021.
Знайти повний текст джерелаMultidimensional Stationary Time Series: Dimension Reduction and Prediction. Taylor & Francis Group, 2021.
Знайти повний текст джерелаMultidimensional Stationary Time Series: Dimension Reduction and Prediction. CRC Press LLC, 2023.
Знайти повний текст джерелаBolla, Marianna. Multidimensional Stationary Time Series. Taylor & Francis Group, 2021.
Знайти повний текст джерелаGreco, Luca, and Alessio Farcomeni. Robust Methods for Data Reduction. Taylor & Francis Group, 2015.
Знайти повний текст джерелаRobust Methods for Data Reduction. Taylor & Francis Group, 2016.
Знайти повний текст джерелаMultilabel Dimensionality Reduction. CRC Press, 2012.
Знайти повний текст джерелаYe, Jieping, Shuiwang Ji, and Liang Sun. Multi-Label Dimensionality Reduction. Taylor & Francis Group, 2016.
Знайти повний текст джерелаЧастини книг з теми "Réduction de dimension (Statistique)"
CHIQUET, Julien, Marie-Josée CROS, Mahendra MARIADASSOU, Nathalie PEYRARD, and Stéphane ROBIN. "Le modèle Poisson log-normal pour l’analyse de distributions jointes d’abondance." In Approches statistiques pour les variables cachées en écologie, 175–99. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9047.ch8.
Повний текст джерелаBYSTROVA, Daria, Giovanni POGGIATO, Julyan ARBEL, and Wilfried THUILLER. "Réduction de la dimension dans les modèles de distributions jointes d’espèces." In Approches statistiques pour les variables cachées en écologie, 151–74. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9047.ch7.
Повний текст джерела"III. Réduction de dimension." In Régression avec R - 2e édition, 157–246. EDP Sciences, 2021. http://dx.doi.org/10.1051/978-2-7598-2183-9-005.
Повний текст джерела"III. Réduction de dimension." In Régression avec R - 2e édition, 157–246. EDP Sciences, 2021. http://dx.doi.org/10.1051/978-2-7598-2183-9.c005.
Повний текст джерела"La dimension conceptuelle." In Réduction de la fracture numérique en tourisme, 1–16. Presses de l'Université du Québec, 2003. http://dx.doi.org/10.2307/j.ctv18pgvv5.8.
Повний текст джерела"4 Mécanique statistique classique : une dimension." In Transitions de phase et groupe de renormalisation, 89–120. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-0150-3-006.
Повний текст джерела"4 Mécanique statistique classique : une dimension." In Transitions de phase et groupe de renormalisation, 89–120. EDP Sciences, 2020. http://dx.doi.org/10.1051/978-2-7598-0150-3.c006.
Повний текст джерелаSchaetzel, Françoise. "La réduction des inégalités sociales de santé : une dimension de la performance." In Améliorer la performance des systèmes de santé, 125–38. Les Presses de l’Université de Montréal, 2017. http://dx.doi.org/10.1515/9782760637542-008.
Повний текст джерелаKUZNETSOV, Igor, and Nickolay KUZNETSOV. "Méthodes de simulation rapide en files d’attente pour la résolution de certains problèmes combinatoires de grande taille." In Théorie des files d’attente 1, 167–205. ISTE Group, 2021. http://dx.doi.org/10.51926/iste.9001.ch6.
Повний текст джерелаRipoll, Élodie. "« Ses yeux brillaient d’un éclat singulier »." In La couleur en questions, 103–12. Hermann, 2023. http://dx.doi.org/10.3917/herm.menu.2023.01.0103.
Повний текст джерелаЗвіти організацій з теми "Réduction de dimension (Statistique)"
de Marcellis-Warin, Nathalie, François Vaillancourt, Ingrid Peignier, Molivann Panot, Thomas Gleize, and Simon Losier. Obstacles et incitatifs à l’adoption des technologies innovantes dans le secteur minier québécois. CIRANO, May 2024. http://dx.doi.org/10.54932/dlxt6536.
Повний текст джерелаDufour, Quentin, David Pontille, and Didier Torny. Contracter à l’heure de la publication en accès ouvert. Une analyse systématique des accords transformants. Ministère de l'enseignement supérieur et de la recherche, April 2021. http://dx.doi.org/10.52949/2.
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