Dissertations / Theses on the topic 'Analyse topologiques des données'
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El, Golli Aicha. "Extraction de données symboliques et cartes topologiques : Application aux données ayant une structure complexe." Paris 9, 2004. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2004PA090026.
Full textEl, Golli Aïcha. "Extraction de données symboliques et cartes topologiques: application aux données ayant une structure complexe." Phd thesis, Université Paris Dauphine - Paris IX, 2004. http://tel.archives-ouvertes.fr/tel-00178900.
Full textVidal, Jules. "Progressivité en analyse topologique de données." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS398.
Full textTopological Data Analysis (TDA) forms a collection of tools that enable the generic and efficient extraction of features in data. However, although most TDA algorithms have practicable asymptotic complexities, these methods are rarely interactive on real-life datasets, which limits their usability for interactive data analysis and visualization. In this thesis, we aimed at developing progressive methods for the TDA of scientific scalar data, that can be interrupted to swiftly provide a meaningful approximate output and that are able to refine it otherwise. First, we introduce two progressive algorithms for the computation of the critical points and the extremum-saddle persistence diagram of a scalar field. Next, we revisit this progressive framework to introduce an approximation algorithm for the persistence diagram of a scalar field, with strong guarantees on the related approximation error. Finally, in a effort to perform visual analysis of ensemble data, we present a novel progressive algorithm for the computation of the discrete Wasserstein barycenter of a set of persistence diagrams, a notoriously computationally intensive task. Our progressive approach enables the approximation of the barycenter within interactive times. We extend this method to a progressive, time-constraint, topological ensemble clustering algorithm
Doan, Nath-Quang. "Modèles hiérarchiques et topologiques pour le clustering et la visualisation des données." Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_doan.pdf.
Full textThis thesis focuses on clustering approaches inspired from topological models and an autonomous hierarchical clustering method. The clustering problem becomes more complicated and difficult due to the growth in quality and quantify of structured data such as graphs, trees or sequences. In this thesis, we are particularly interested in self-organizing maps which have been generally used for learning topological preservation, clustering, vector quantization and graph visualization. Our studyconcerns also a hierarchical clustering method AntTree which models the ability of real ants to build structure by connect themselves. By combining the topological map with the self-assembly rules inspired from AntTree, the goal is to represent data in a hierarchical and topological structure providing more insight data information. The advantage is to visualize the clustering results as multiple hierarchical trees and a topological network. In this report, we present three new models that are able to address clustering, visualization and feature selection problems. In the first model, our study shows the interest in the use of hierarchical and topological structure through several applications on numerical datasets, as well as structured datasets e. G. Graphs and biological dataset. The second model consists of a flexible and growing structure which does not impose the strict network-topology preservation rules. Using statistical characteristics provided by hierarchical trees, it accelerates significantly the learning process. The third model addresses particularly the issue of unsupervised feature selection. The idea is to use hierarchical structure provided by AntTree to discover automatically local data structure and local neighbors. By using the tree topology, we propose a new score for feature selection by constraining the Laplacian score. Finally, this thesis offers several perspectives for future work
Rogouschi, Nicoleta. "Classification à base de modèles de mélanges topologiques des données catégorielles et continues." Paris 13, 2009. http://www.theses.fr/2009PA132015.
Full textThe research presented in this thesis concerns the development of self-organising map approaches based on mixture models which deal with different kinds of data : qualitative, mixed and sequential. For each type of data we propose an adapted unsupervised learning model. The first model, described in this work, is a new learning algorithm of topological map BeSOM (Bernoulli Self-Organizing Map) dedicated to binary data. Each map cell is associated with a Bernoulli distribution. In this model, the learning has the objective to estimate the density function presented as a mixture of densities. Each density is as well a mixture of Bernoulli distribution defined on a neighbourhood. The second model touches upon the problem of probability approaches for the mixeddata clustering (quantitative and qualitative). The model is inspired by previous workswhich define a distribution by a mixture of Bernoulli and Gaussian distributions. This approach gives a different dimension to topological map : it allows probability map interpretation and others the possibility to take advantage of local distribution associated with continuous and categorical variables. As for the third model presented in this thesis, it is a new Markov mixture model applied to treatment of the data structured in sequences. The approach that we propose is a generalisation of traditional Markov chains. There are two versions : the global approach, where topology is used implicitly, and the local approach where topology is used explicitly. The results obtained upon the validation of all the methods are encouragingand promising, both for classification and modelling
Jaziri, Rakia. "Modèles de mélanges topologiques pour la classification de données structurées en séquences." Paris 13, 2013. http://scbd-sto.univ-paris13.fr/secure/edgalilee_th_2013_jaziri.pdf.
Full textRecent years have seen the development of data mining techniques in various application areas, with the purpose of analyzing sequential, large and complex data. In this work, the problem of clustering, visualization and structuring data is tackled by a three-stage proposal. The first proposal present a generative approach to learn a new probabilistic Self-Organizing Map (PrSOMS) for non independent and non identically distributed data sets. Our model defines a low dimensional manifold allowing friendly visualizations. To yield the topology preserving maps, our model exhibits the SOM like learning behavior with the advantages of probabilistic models. This new paradigm uses HMM (Hidden Markov Models) formalism and introduces relationships between the states. This allows us to take advantage of all the known classical views associated to topographic map. The second proposal concerns a hierarchical extension of the approach PrSOMS. This approach deals the complex aspect of the data in the classification process. We find that the resulting model ”H-PrSOMS” provides a good interpretability of classes built. The third proposal concerns an alternative approach statistical topological MGTM-TT, which is based on the same paradigm than HMM. It is a generative topographic modeling observation density mixtures, which is similar to a hierarchical extension of time GTM model. These proposals have then been applied to test data and real data from the INA (National Audiovisual Institute). This work is to provide a first step, a finer classification of audiovisual broadcast segments. In a second step, we sought to define a typology of the chaining of segments (multiple scattering of the same program, one of two inter-program) to provide statistically the characteristics of broadcast segments. The overall framework provides a tool for the classification and structuring of audiovisual programs
Lebbah, Mustapha. "Carte topologique pour données qualitatives : application à la reconnaissance automatique de la densité du trafic routier." Phd thesis, Université de Versailles-Saint Quentin en Yvelines, 2003. http://tel.archives-ouvertes.fr/tel-00161698.
Full textCe travail de thèse a été réalisé à la direction de la recherche de RENAULT. Le travail s'est focalisé sur le développement d'un modèle de reconnaissance de trafic.
Le premier modèle proposé dans cette thèse est dédié aux données binaires ''BTM''. C'est un modèle de quantification vectorielle de type carte topologique. Ce modèle prend les caractéristiques principales des cartes topologiques. Pour ce nouveau modèle, afin de prendre en compte les spécificités des données binaires, on a choisi de changer la métrique usuelle utilisée dans les modèles de cartes topologiques et d'utiliser la distance Hamming.
Le second modèle est le premier modèle probabiliste de cartes topologiques dédié aux données binaires. Ce modèle s'inspire de travaux antérieurs qui modélisent une distribution par un mélange de mélange de lois de Bernoulli.
Le troisième modèle est un nouveau modèle de carte topologique appelé CTM (Categorical topological Map) adapté à la classification non supervisée des données qualitatives multi-dimensionnelles. Ce nouveau modèle conserve cependant les principales caractéristiques des deux modèles précédents. Afin de maximiser les vraisemblance des données, CTM utilise de manière classique l'algorithme EM.
Dans ce mémoire, on introduit le domaine d'application propre au travail mené chez RENAULT. Nous détaillerons l'apport original de notre travail: utilisation de l'information catégorielle pour traiter de la reconnaissance du trafic. Nous exposerons les différentes analyses effectuées sur l'application des algorithmes proposés.
Lacombe, Théo. "Statistiques sur les descripteurs topologiques à base de transport optimal." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX036.
Full textTopological data analysis (TDA) allows one to extract rich information from structured data (such as graphs or time series) that occurs in modern machine learning problems. This information will be represented as descriptors such as persistence diagrams, which can be described as point measures supported on a half-plane. While persistence diagrams are not elements of a vector space, they can still be compared using partial matching metrics. The similarities between these metrics and those routinely used in optimal transport—another field of mathematics—are known for long, but a formal connection between these two fields is yet to come.The purpose of this thesis is to clarify this connection and develop new theoretical and computational tools to manipulate persistence diagrams, targeting statistical applications. First, we show how optimal partial transport with boundary, a variation of classic optimal transport theory, provides a formalism that encompasses standard metrics in TDA. We then show-case the benefits of this connection in different situations: a theoretical study and the development of an algorithm to perform fast estimation of barycenters of persistence diagrams, the characterization of continuous linear representations of persistence diagrams and how to learn such representations using a neural network, and eventually a stability result in the context of linearly averaging random persistence diagrams
Alboody, Ahed. "Réception des données spatiales et leurs traitements : analyse d'images satellites pour la mise à jour des SIG par enrichissement du système de raisonnement spatial RCC8." Toulouse 3, 2011. http://thesesups.ups-tlse.fr/1316/.
Full textNowadays, the resolution of satellite images and the volume of available geographic databases are constantly growing. Images of high resolution remote sensing represent sources of heterogeneous data increasingly necessary and difficult to exploit. These images are considered very rich and useful sources for updating Geographic Information Systems (GIS). To update these databases, a step of change detection is necessary and required. This thesis focuses on the study of satellite image analysis by enriching the spatial reasoning system RCC8 (Region Connection Calculus) for the detection of topological changes in order to update GIS databases. The ultimate goal of this study is to exploit and enrich the topological relations of the system RCC8. The interest of the enrichment and detailed description of RCC8 system relations lies in the fact that they can automatically detect the different levels of topological details and topological changes between geographical regions represented on GIS digital maps and satellite images. In this thesis, we propose and develop an extension of the Intersection and Difference (ID) topological model by using topological invariants which are : the separation number, the neighborhood and the spatial element type. This extension enriches and details the relations of the system RCC8 at two levels of detail. At the first level, the enrichment of the system RCC8 is made by using the topological invariant of the separation number and the new system is called "system RCC-16 at level-1". To avoid confusion problems between the topological relations of this new system, the second level by enriching the "system RCC-16 at level-1" is done by using the topological invariant of the spatial element type and the new system is called "system RCC-16 at level-2". These two systems RCC-16 (at two levels : level-1 and level-2) will be applied to satellite image analysis, change detection and spatial analysis in GIS. We propose a new method for detecting changes between a new satellite image and a GIS old digital map. This method integrates the topological analysis of the system RCC-16 to detect and identify changes between two satellite images, or between two vector maps produced at different dates. In this study of the enrichment of the system RCC8, spatial regions have simple spatial representations. However, the spatial and topological relations between regions in satellite images and GIS data are more complex, vague and uncertain. With the aim of studying the topological relations between fuzzy regions, a model called the Fuzzy topological model of Intersection and Difference (FID) for the description of topological relations between fuzzy regions is proposed and developed. 152 topological relations can be extracted using this model FID. These 152 relations are grouped into eight clusters of the qualitative relations of the system RCC8 : Disjoint (Disconnected), Meets (Externally Connected), Overlaps (Partially Overlapping), CoveredBy (Tangential Proper Part), Inside (Non-Tangential Proper Part), Covers (Tangential Proper Part Inverse), Contains (Non-Tangential Proper Part Inverse), and Equal. These relations will be evaluated and extracted from satellite images to give examples of their interest in the image analysis field and GIS. The contribution of this thesis is marked by enriching the qualitative spatial reasoning system RCC8 giving rise to a new system, RCC-16, implementing a new method of change detection, the model FID, and clustering the 152 fuzzy topological relations in eight qualitative clusters of the system RCC8
Soler, Maxime. "Réduction et comparaison de structures d'intérêt dans des jeux de données massifs par analyse topologique." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS364.
Full textIn this thesis, we propose different methods, based on topological data analysis, in order to address modern problematics concerning the increasing difficulty in the analysis of scientific data. In the case of scalar data defined on geometrical domains, extracting meaningful knowledge from static data, then time-varying data, then ensembles of time-varying data proves increasingly challenging. Our approaches for the reduction and analysis of such data are based on the idea of defining structures of interest in scalar fields as topological features. In a first effort to address data volume growth, we propose a new lossy compression scheme which offers strong topological guarantees, allowing topological features to be preserved throughout compression. The approach is shown to yield high compression factors in practice. Extensions are proposed to offer additional control over the geometrical error. We then target time-varying data by designing a new method for tracking topological features over time, based on topological metrics. We extend the metrics in order to overcome robustness and performance limitations. We propose a new efficient way to compute them, gaining orders of magnitude speedups over state-of-the-art approaches. Finally, we apply and adapt our methods to ensemble data related to reservoir simulation, for modeling viscous fingering in porous media. We show how to capture viscous fingers with topological features, adapt topological metrics for capturing discrepancies between simulation runs and a ground truth, evaluate the proposed metrics with feedback from experts, then implement an in-situ ranking framework for rating the fidelity of simulation runs
Razafindramanana, Octavio. "Low-dimensional data analysis and clustering by means of Delaunay triangulation." Thesis, Tours, 2014. http://www.theses.fr/2014TOUR4033/document.
Full textThis thesis aims at proposing and discussing several solutions to the problem of low-dimensional point cloudanalysis and clustering. These solutions are based on the analysis of the Delaunay triangulation.Two types of approaches are presented and discussed. The first one follows a classical three steps approach:1) the construction of a proximity graph that embeds topological information, 2) the construction of statisticalinformation out of this graph and 3) the removal of pointless elements regarding this information. The impactof different simplicial complex-based measures, i.e. not only based on a graph, is discussed. Evaluation is madeas regards point cloud clustering quality along with handwritten character recognition rates. The second type ofapproaches consists of one-step approaches that derive clustering along with the construction of the triangulation
Gueunet, Charles. "Calcul haute performance pour l'analyse topologique de données par ensembles de niveaux." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS120.
Full textTopological Data Analysis requires efficient algorithms to deal with the continuously increasing size and level of details of data sets. In this manuscript, we focus on three fundamental topological abstractions based on level sets: merge trees, contour trees and Reeb graphs. We propose three new efficient parallel algorithms for the computation of these abstractions on multi-core shared memory workstations. The first algorithm developed in the context of this thesis is based on multi-thread parallelism for the contour tree computation. A second algorithm revisits the reference sequential algorithm to compute this abstraction and is based on local propagations expressible as parallel tasks. This new algorithm is in practice twice faster in sequential than the reference algorithm designed in 2000 and offers one order of magnitude speedups in parallel. A last algorithm also relying on task-based local propagations is presented, computing a more generic abstraction: the Reeb graph. Contrary to concurrent approaches, these methods provide the augmented version of these structures, hence enabling the full extend of level-set based analysis. Algorithms presented in this manuscript result today in the fastest implementations available to compute these abstractions. This work has been integrated into the open-source platform: the Topology Toolkit (TTK)
Bonis, Thomas. "Algorithmes d'apprentissage statistique pour l'analyse géométrique et topologique de données." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLS459/document.
Full textIn this thesis, we study data analysis algorithms using random walks on neighborhood graphs, or random geometric graphs. It is known random walks on such graphs approximate continuous objects called diffusion processes. In the first part of this thesis, we use this approximation result to propose a new soft clustering algorithm based on the mode seeking framework. For our algorithm, we want to define clusters using the properties of a diffusion process. Since we do not have access to this continuous process, our algorithm uses a random walk on a random geometric graph instead. After proving the consistency of our algorithm, we evaluate its efficiency on both real and synthetic data. We then deal tackle the issue of the convergence of invariant measures of random walks on random geometric graphs. As these random walks converge to a diffusion process, we can expect their invariant measures to converge to the invariant measure of this diffusion process. Using an approach based on Stein's method, we manage to obtain quantitfy this convergence. Moreover, the method we use is more general and can be used to obtain other results such as convergence rates for the Central Limit Theorem. In the last part of this thesis, we use the concept of persistent homology, a concept of algebraic topology, to improve the pooling step of the bag-of-words approach for 3D shapes
Cochoy, Jérémy. "Decomposability and stability of multidimensional persistence." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS566/document.
Full textIn a context where huge amounts of data are available, extracting meaningful and non trivial information is getting harder. In order to improve the tasks of classification, regression, or exploratory analysis, the approach provided by topological data analysisis to look for the presence of shapes in data set.In this thesis, we investigate the properties of multidimensional persistence modules in order to obtain a better understanding of the summands and decompositions of such modules. We introduce a functor that embeds the representations category of any quiver whose graph is a rooted tree into the category of ℝ²-indexed persistence modules. We also enrich the structure of persistence module arising from the cohomology of a filtration to a structure of persistence algebra.Finally, we generalize the approach of Crawley Beovey to multipersistence and identify a class of persistencemodules indexed on ℝ² which have simple descriptor and an analog of the decomposition theorem available in one dimensional persistence
Carriere, Mathieu. "On Metric and Statistical Properties of Topological Descriptors for geometric Data." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS433/document.
Full textIn the context of supervised Machine Learning, finding alternate representations, or descriptors, for data is of primary interest since it can greatly enhance the performance of algorithms. Among them, topological descriptors focus on and encode the topological information contained in geometric data. One advantage of using these descriptors is that they enjoy many good and desireable properties, due to their topological nature. For instance, they are invariant to continuous deformations of data. However, the main drawback of these descriptors is that they often lack the structure and operations required by most Machine Learning algorithms, such as a means or scalar products. In this thesis, we study the metric and statistical properties of the most common topological descriptors, the persistence diagrams and the Mappers. In particular, we show that the Mapper, which is empirically instable, can be stabilized with an appropriate metric, that we use later on to conpute confidence regions and automatic tuning of its parameters. Concerning persistence diagrams, we show that scalar products can be defined with kernel methods by defining two kernels, or embeddings, into finite and infinite dimensional Hilbert spaces
Bou, Dagher Lea. "Analyse biogéométrique de l'évolution des protéines et des traits d'histoire de vie." Electronic Thesis or Diss., Lyon 1, 2024. http://www.theses.fr/2024LYO10277.
Full textBeyond their functional role in cells, proteins serve as important material in evolutionary biology because they contain a historical (i.e. phylogenetic) signal that can be used to retrace their evolutionary history, as well as that of organisms. This signal is traditionally studied using molecular phylogeny methods based on the comparison of protein sequences. However, the analysis of 3D protein structures has been proposed as an interesting alternative. Indeed, structures evolve more slowly than sequences, offering access to a more ancient phylogenetic signal. On the other hand, protein sequences play also a key role for studying adaptive processes, such as adaptation to environmental temperature, salinity or pressure. The optimal temperature at which microorganisms live imposes very strong constraints on proteins, particularly on the preferential use of certain amino acids. As a result, the amino acid composition of organisms’ proteomes is linked to their optimal growth temperature. Environmental temperature also exerts constraints that affect the 3D structures. This thesis aims to study the 3D structures using methods derived from topological data analysis. We introduce persistent homology methods to analyze the geometric features of 3D structures, as well as the information they contain such as their evolutionary history (phylogenetic signal) and their adaptation to temperature. First, we show that persistent homology captures a phylogenetic signal in 3D structures. We then define a vectorization of 3D structures weighted by their physicochemical properties and based on the topological descriptors of persistent homology. This approach makes it possible to refine the estimation of evolutionary distances. We combine these vectorizations with machine learning methods to estimate the optimal growth temperatures for a major group of archaea, the Methanococcales. Secondly, we carry out a spectral analysis of the Laplacians associated with the 3D structures. The Laplacian captures not only the topological invariants of a point cloud in its harmonic spectrum, such as those provided by persistent homology, but also captures geometric features related to the curvature of the point cloud. We establish lower and upper bounds theorems for the curvature of a discrete space by spectral values of its persistent Laplacian. Finally, we propose a predictive model for estimating the optimal growth temperatures of organisms based on the spectral analysis of their 3D structures
Grozavu, Nistor. "Classification topologique pondérée : approches modulaires, hybrides et collaboratives." Paris 13, 2009. http://www.theses.fr/2009PA132022.
Full textThis thesis is focused, on the one hand, to study clustering anlaysis approaches in an unsupervised topological learning, and in other hand, to the topological modular, hybrid and collaborative clustering. This study is adressed mainly on two problems: - cluster characterization using weighting and selection of relevant variables, and the use of the memory concept during the learning unsupervised topological process; - and the problem of the ensemble clustering techniques : the modularization, the hybridization and collaboration. We are particularly interested in this thesis in Kohonen's self-organizing maps which have been widely used for unsupervised classification and visualization of multidimensional datasets. We offer several weighting approaches and a new strategy which consists in the introduction of a memory process into the competition phase by calculating a voting matrix at each learning iteration. Using a statistical test for selecting relevant variables, we will respond to the problem of dimensionality reduction, and to the problem of the cluster characterization. For the second problem, we use the relational analysis approach (RA) to combine multiple topological clustering results
Pritam, Siddharth. "Effondrements et homologie persistante." Thesis, Université Côte d'Azur, 2020. https://tel.archives-ouvertes.fr/tel-02962587.
Full textIn this thesis, we introduce two new approaches to compute the Persistent Homology (PH) of a sequence of simplicial complexes. The basic idea is to simplify the complexes of the input sequence by using special types of collapses (strong and edge collapse) and to compute the PH of an induced sequence of smaller size that has the same PH as the initial one.Our first approach uses strong collapse which is introduced by J. Barmak and E. Miniam [DCG (2012)]. Strong collapse comprises of removal of special vertices called \textit{dominated} vertices from a simplicial complex.Our approach with strong collapse has several salient features that distinguishes it from previous work. It is not limited to filtrations (i.e. sequences of nested simplicial subcomplexes) but works for othertypes of sequences like towers and zigzags. To strong collapse a simplicial complex, we only need to store the maximal simplices of the complex, not the full set of all its simplices, which saves a lot ofspace and time. Moreover, the complexes in the sequence can be strong collapsed independently and in parallel.In the case of flag complexes strong collapse can be performed over the $1$-skeleton of the complex and the resulting complex is also a flag complex. We show that if we restrict the class of simplicial complexes to flag complexes, we can achieve decisive improvement in terms of time and space complexities with respect to previous work. When we strong collapse the complexes in a flag tower, we obtain a reduced sequence that is also a flag tower we call the coreflag tower. We then convert the core flag tower to an equivalent filtration to compute its PH. Here again, we only use the 1-skeletons of the complexes. The resulting method is simple and extremelyefficient. We extend the notions of dominated vertex to a simplex of any dimension. Domination of edges appear to be very powerful and we study it in the case of flag complexes in more detail. We show that edge collapse (removal of dominated edges) in a flag complex can be performed using only the 1-skeleton of the complex as well. Furthermore, the residual complex is a flag complex as well. Next we show that, similar to the case of strong collapses, we can use edge collapses to reduce a flag filtration F to a smaller flag filtration F^c with the same persistence. Here again, we only use the 1-skeletons of the complexes. As a result and as demonstrated by numerous experiments on publicly available data sets, our approaches are extremely fast and memory efficient in practice. In particular the method using edge collapse performs the best among all known methods including the strong collapse approach. Finally, we can compromizebetween precision and time by choosing the number of simplicial complexes of the sequence we strong collapse
Pont, Mathieu. "Analysis of Ensembles of Topological Descriptors." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS436.
Full textTopological Data Analysis (TDA) forms a collection of tools to generically, robustly and efficiently reveal implicit structural patterns hidden in complex datasets. These tools allow to compute a topological representation for each member of an ensemble of datasets by encoding its main features of interest in a concise and informative manner. A major challenge consists then in designing analysis tools for such ensembles of topological descriptors. Several tools have been well studied for persistence diagrams, one of the most used descriptor. However, they suffer from a lack of specificity, which can yield identical data representations for significantly distinct datasets. In this thesis, we aimed at developing more advanced analysis tools for ensembles of topological descriptors, capable of tackling the lack of discriminability of persistence diagrams and going beyond what was already available for these objects. First, we adapt to merge trees, descriptors having a better specificity, the tools already available for persistence diagrams such as distances, geodesics and barycenters. Then, we want to go beyond this notion of average being the barycenter in order to study the variability within an ensemble of topological descriptors. We then adapt the Principal Component Analysis framework to persistence diagrams and merge trees, resulting in a dimensionality reduction method that indicates which structures in the ensemble are most responsible for the variability. However, this framework allows only to detect linear patterns of variability in the ensemble. To tackle this we propose to generalize this framework to Auto-Encoder in order to detect non-linear, i.e. more complex, patterns in an ensemble of merge trees or persistence diagrams. Specifically, we propose a new neural network layer capable of processing natively these objects. We present applications of all this work in feature tracking in a time-varying ensemble, data reduction to compress an ensemble of topological descriptors, clustering to form homogeneous groups in an ensemble, and dimensionality reduction to create a visual map indicating how the data are organized regarding each other in the ensemble
Tinarrage, Raphaël. "Inférence topologique à partir de mesures et de fibrés vectoriels." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM001.
Full textWe contribute to the theory of topological inference, based on the theory of persistent homology, by proposing three families of filtrations.For each of them, we prove consistency results---that is, the quality of approximation of an underlying geometric object---, and stability results---that is, robustness against initial measurement errors.We propose concrete algorithms in order to use these methods in practice.The first family, the DTM-filtration, is a robust alternative to the classical Cech filtration when the point cloud is noisy or contains outliers.It is based on the notion of distance to measure, which allows to obtain stability in the sense of the Wasserstein distance.Secondly, we propose the lifted filtrations, which make it possible to estimate the homology of immersed manifolds, even when their reach is zero.We introduce the notion of normal reach, and show that it leads to a quantitative control of the manifold.We study the estimation of tangent spaces by local covariance matrices.Thirdly, we develop a framework for vector bundle filtrations, and define the persistent Stiefel-Whitney classes.We show that the persistent classes associated to the Cech bundle filtrations are Hausdorff-stable and consistent.To allow their algorithmic implementation, we introduce the notion of weak star condition
Segoufin, Luc. "Manipulation de données spaciales et topologiques." Paris 11, 1999. http://www.theses.fr/1999PA112033.
Full textBerkouk, Nicolas. "Persistence and Sheaves : from Theory to Applications." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX032.
Full textTopological data analysis is a recent field of research aiming at using techniques coming from algebraic topology to define descriptors of datasets. To be useful in practice, these descriptors must be computable, and coming with a notion of metric, in order to express their stability properties with res-pect to the noise that always comes with real world data. Persistence theory was elaborated in the early 2000’s as a first theoretical setting to define such des-criptors - the now famous so-called barcodes. Howe-ver very well suited to be implemented in a compu-ter, persistence theory has certain limitations. In this manuscript, we establish explicit links between the theory of derived sheaves equipped with the convolu-tion distance (after Kashiwara-Schapira) and persis-tence theory.We start by showing a derived isometry theorem for constructible sheaves over R, that is, we express the convolution distance between two sheaves as a matching distance between their graded barcodes. This enables us to conclude in this setting that the convolution distance is closed, and that the collec-tion of constructible sheaves over R equipped with the convolution distance is locally path-connected. Then, we observe that the collection of zig-zag/level sets persistence modules associated to a real valued function carry extra structure, which we call Mayer-Vietoris systems. We classify all Mayer-Vietoris sys-tems under finiteness assumptions. This allows us to establish a functorial isometric correspondence bet-ween the derived category of constructible sheaves over R equipped with the convolution distance, and the category of strongly pfd Mayer-Vietoris systems endowed with the interleaving distance. We deduce from this result a way to compute barcodes of sheaves from already existing software.Finally, we give a purely sheaf theoretic definition of the notion of ephemeral persistence module. We prove that the observable category of persistence mo-dules (the quotient category of persistence modules by the sub-category of ephemeral ones) is equivalent to the well-known category of -sheaves
Buchet, Mickaël. "Topological inference from measures." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112367/document.
Full textMassive amounts of data are now available for study. Asking questions that are both relevant and possible to answer is a difficult task. One can look for something different than the answer to a precise question. Topological data analysis looks for structure in point cloud data, which can be informative by itself but can also provide directions for further questioning. A common challenge faced in this area is the choice of the right scale at which to process the data.One widely used tool in this domain is persistent homology. By processing the data at all scales, it does not rely on a particular choice of scale. Moreover, its stability properties provide a natural way to go from discrete data to an underlying continuous structure. Finally, it can be combined with other tools, like the distance to a measure, which allows to handle noise that are unbounded. The main caveat of this approach is its high complexity.In this thesis, we will introduce topological data analysis and persistent homology, then show how to use approximation to reduce the computational complexity. We provide an approximation scheme to the distance to a measure and a sparsifying method of weighted Vietoris-Rips complexes in order to approximate persistence diagrams with practical complexity. We detail the specific properties of these constructions.Persistent homology was previously shown to be of use for scalar field analysis. We provide a way to combine it with the distance to a measure in order to handle a wider class of noise, especially data with unbounded errors. Finally, we discuss interesting opportunities opened by these results to study data where parts are missing or erroneous
Memari, Pooran. "Tomographie géométrique avec garanties topologiques." Phd thesis, Université de Nice Sophia-Antipolis, 2010. http://tel.archives-ouvertes.fr/tel-00560010.
Full textUntereiner, Lionel. "Représentation des maillages multirésolutions : application aux volumes de subdivision." Phd thesis, Université de Strasbourg, 2013. http://tel.archives-ouvertes.fr/tel-00951049.
Full textGodoy, Campbell Matias. "Sur le problème inverse de détection d'obstacles par des méthodes d'optimisation." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30220/document.
Full textThis PhD thesis is dedicated to the study of the inverse problem of obstacle/object detection using optimization methods. This problem consists in localizing an unknown object omega inside a known bounded domain omega by means of boundary measurements and more precisely by a given Cauchy pair on a part Gammaobs of thetaOmega. We cover the scalar and vector scenarios for this problem considering both the Laplace and the Stokes equations. For both cases, we rely on identifiability result which ensures that there is a unique obstacle/object which corresponds to the considered boundary measurements. The strategy used in this work is to reduce the inverse problem into the minimization of a cost-type functional: the Kohn-Vogelius functional. This kind of approach is widely used and permits to use optimization tools for numerical implementations. However, in order to well-define the functional, this approach needs to assume the knowledge of a measurement on the whole exterior boundary thetaOmega. This last point leads us to first study the data completion problem which consists in recovering the boundary conditions on an inaccessible region, i.e. on thetaOmega\Gammaobs, from the Cauchy data on the accessible region Gammaobs. This inverse problem is also studied through the minimization of a Kohn-Vogelius type functional. The ill-posedness of this problem enforces us to regularize the functional via a Tikhonov regularization. We obtain several theoretical properties as convergence properties, in particular when data is corrupted by noise. Based on these theoretical results, we reconstruct numerically the boundary data by implementing a gradient algorithm in order to minimize the regularized functional. Then we study the obstacle detection problem when only partial boundary measurements are available. We consider the inaccessible boundary conditions and the unknown object as the variables of the functional and then, using geometrical shape optimization tools, in particular the shape gradient of the Kohn-Vogelius functional, we perform the numerical reconstruction of the unknown inclusion. Finally, we consider, into the two dimensional vector case, a new degree of freedom by studying the case when the number of objects is unknown. Hence, we use the topological shape optimization in order to minimize the Kohn-Vogelius functional. We obtain the topological asymptotic expansion of the solution of the 2D Stokes equations and characterize the topological gradient for this functional. Then we determine numerically the number and location of the obstacles. Additionally, we propose a blending algorithm which combines the topological and geometrical shape optimization methods in order to determine numerically the number, location and shape of the objects
Marine, Cadoret. "Analyse factorielle de données de catégorisation. : Application aux données sensorielles." Rennes, Agrocampus Ouest, 2010. http://www.theses.fr/2010NSARG006.
Full textIn sensory analysis, holistic approaches in which objects are considered as a whole are increasingly used to collect data. Their interest comes on a one hand from their ability to acquire other types of information as the one obtained by traditional profiling methods and on the other hand from the fact they require no special skills, which makes them feasible by all subjects. Categorization (or free sorting), in which subjects are asked to provide a partition of objects, belongs to these approaches. The first part of this work focuses on categorization data. After seeing that this method of data collection is relevant, we focus on the statistical analysis of these data through the research of Euclidean representations. The proposed methodology which consists in using factorial methods such as Multiple Correspondence Analysis (MCA) or Multiple Factor Analysis (MFA) is also enriched with elements of validity. This methodology is then illustrated by the analysis of two data sets obtained from beers on a one hand and perfumes on the other hand. The second part is devoted to the study of two data collection methods related to categorization: sorted Napping® and hierarchical sorting. For both data collections, we are also interested in statistical analysis by adopting an approach similar to the one used for categorization data. The last part is devoted to the implementation in the R software of functions to analyze the three kinds of data that are categorization data, hierarchical sorting data and sorted Napping® data
Gomes, Da Silva Alzennyr. "Analyse des données évolutives : application aux données d'usage du Web." Phd thesis, Université Paris Dauphine - Paris IX, 2009. http://tel.archives-ouvertes.fr/tel-00445501.
Full textGomes, da Silva Alzennyr. "Analyse des données évolutives : Application aux données d'usage du Web." Paris 9, 2009. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=2009PA090047.
Full textNowadays, more and more organizations are becoming reliant on the Internet. The Web has become one of the most widespread platforms for information change and retrieval. The growing number of traces left behind user transactions (e. G. : customer purchases, user sessions, etc. ) automatically increases the importance of usage data analysis. Indeed, the way in which a web site is visited can change over time. These changes can be related to some temporal factors (day of the week, seasonality, periods of special offer, etc. ). By consequence, the usage models must be continuously updated in order to reflect the current behaviour of the visitors. Such a task remains difficult when the temporal dimension is ignored or simply introduced into the data description as a numeric attribute. It is precisely on this challenge that the present thesis is focused. In order to deal with the problem of acquisition of real usage data, we propose a methodology for the automatic generation of artificial usage data over which one can control the occurrence of changes and thus, analyse the efficiency of a change detection system. Guided by tracks born of some exploratory analyzes, we propose a tilted window approach for detecting and following-up changes on evolving usage data. In order measure the level of changes, this approach applies two external evaluation indices based on the clustering extension. The proposed approach also characterizes the changes undergone by the usage groups (e. G. Appearance, disappearance, fusion and split) at each timestamp. Moreover, the refereed approach is totally independent of the clustering method used and is able to manage different kinds of data other than usage data. The effectiveness of this approach is evaluated on artificial data sets of different degrees of complexity and also on real data sets from different domains (academic, tourism, e-business and marketing)
Cailhol, Simon. "Planification interactive de trajectoire en Réalité Virtuelle sur la base de données géométriques, topologiques et sémantiques." Thesis, Toulouse, INPT, 2015. http://www.theses.fr/2015INPT0058/document.
Full textTo save time and money while designing new products, industry needs tools to design, test and validate the product using virtual prototypes. These virtual prototypes must enable to test the product at all Product Lifecycle Management (PLM) stages. Many operations in product’s lifecycle involve human manipulation of product components (product assembly, disassembly or maintenance). Cue to the increasing integration of industrial products, these manipulations are performed in cluttered environment. Virtual Reality (VR) enables real operators to perform these operations with virtual prototypes. This research work introduces a novel path planning architecture allowing collaboration between a VR user and an automatic path planning system. This architecture is based on an original environment model including semantic, topological and geometric information. The automatic path planning process split in two phases. First, coarse planning uses semantic and topological information. This phase defines a topological path. Then, fine planning uses semantic and geometric information to define a geometrical trajectory within the topological path defined by the coarse planning. The collaboration between VR user and automatic path planner is made of two modes: on one hand, the user is guided along a pre-computed path through a haptic device, on the other hand, the user can go away from the proposed solution and doing it, he starts a re-planning process. Efficiency and ergonomics of both interaction modes is improved thanks to control sharing methods. First, the authority of the automatic system is modulated to provide the user with a sensitive guidance while he follows it and to free the user (weakened guidance) when he explores possible better ways. Second, when the user explores possible better ways, his intents are predicted (thanks to geometrical data associated to topological elements) and integrated in the re-planning process to guide the coarse planning. This thesis is divided in five chapters. The first one exposes the industrial context that motivated this work. Following a description of environment modeling tools, the second chapter introduces the multi-layer environment model proposed. The third chapter presents the path planning techniques from robotics research and details the two phases path planning process developed. The fourth introduce previous work on interactive path planning and control sharing techniques before to describe the interaction modes and control sharing techniques involved in our interactive path planner. Finally, last chapter introduces the experimentations performed with our path planner and analyses their results
Nauleau, Florent. "Méthode des frontières immergées pour la simulation aux grandes échelles de véhicules de rentrée hypersoniques." Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0477.
Full textThe aim of this thesis is to develop a simulation code for the design of atmospheric reentry vehicles. The code used is an immersed boundary code, which considerably reduces the time required to generate complex meshes. Several implementations within the code reduce computation time while increasing accuracy. The study of complex phenomena developing around simulated objects will be aided by topological analysis, helping in the choice of numerical method to be used. From a numerical point of view, the design of an atmospheric reentry vehicle for its aerothermal performance often relies on computational codes using averaged Navier-Stokes equations (RANS) and body-fitted structured meshes. These two technologies enable us to obtain an averaged representation of the phenomena in a reasonable time. However, the use of an averaged field implies less control over the maximum stresses that could be applied to the vehicle, and the generation of these body-fitted meshes is extremely time-consuming. From a visualization point of view, traditional analysis methods are based on flow geometry and field-averaged quantities. Due to high Mach and Reynolds numbers and the geometric complexity of flows, these methods are often pushed to the limits of their applicability, or even rendered obsolete for vortex segmentation and comparison. The aim of this thesis is to provide some answers to the above-mentioned numerical and scientific visualization concerns. To improve immersed boundary methods, new Riemann solvers and high-order reconstruction schemes such as TENO and WENO have been integrated within a Direct Numerical Simulation (DNS) code. To reduce the mesh cost of DNS simulations, the Wall-Adapting Local Eddy-Viscosity (WALE) subgrid-scale model has been implemented. This model able Large Eddy Simulation (LES) to be carried out. In these simulations, the larger vortices are computed and the smaller ones modeled. Boundary layer capture, i.e. aerodynamic and thermal effects at the vehicle wall, is investigated by proposing wall models for hypersonic flows. These wall models will make it possible to reduce the number of cells and thus the computational cost of modeling the boundary layer. Topological data analysis is a particularly interesting emerging approach to apprehend the quantity and complexity of data generated in aerodynamics. This field, born of computer science and applied mathematics, proposes to extract, measure and compare structural information hidden within large volumes of complex data. Based on projection and dimension reduction techniques, these approaches extract features from data that are difficult to identify in geometric space, and complement the functionalities of high-performance visualization software such as Paraview. Topological analysis protocols have been proposed to compare and validate the new Riemann solvers and high-order reconstructions implemented in this thesis. These protocols have been applied to 2D turbulence, and have enabled us to select pairs of Riemann solvers and high-order reconstructions to reduce the computational cost of simulations while maintaining good accuracy in describing the phenomena studied
Peng, Tao. "Analyse de données loT en flux." Electronic Thesis or Diss., Aix-Marseille, 2021. http://www.theses.fr/2021AIXM0649.
Full textSince the advent of the IoT (Internet of Things), we have witnessed an unprecedented growth in the amount of data generated by sensors. To exploit this data, we first need to model it, and then we need to develop analytical algorithms to process it. For the imputation of missing data from a sensor f, we propose ISTM (Incremental Space-Time Model), an incremental multiple linear regression model adapted to non-stationary data streams. ISTM updates its model by selecting: 1) data from sensors located in the neighborhood of f, and 2) the near-past most recent data gathered from f. To evaluate data trustworthiness, we propose DTOM (Data Trustworthiness Online Model), a prediction model that relies on online regression ensemble methods such as AddExp (Additive Expert) and BNNRW (Bagging NNRW) for assigning a trust score in real time. DTOM consists: 1) an initialization phase, 2) an estimation phase, and 3) a heuristic update phase. Finally, we are interested predicting multiple outputs STS in presence of imbalanced data, i.e. when there are more instances in one value interval than in another. We propose MORSTS, an online regression ensemble method, with specific features: 1) the sub-models are multiple output, 2) adoption of a cost sensitive strategy i.e. the incorrectly predicted instance has a higher weight, and 3) management of over-fitting by means of k-fold cross-validation. Experimentation with with real data has been conducted and the results were compared with reknown techniques
Sibony, Eric. "Analyse mustirésolution de données de classements." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0036/document.
Full textThis thesis introduces a multiresolution analysis framework for ranking data. Initiated in the 18th century in the context of elections, the analysis of ranking data has attracted a major interest in many fields of the scientific literature : psychometry, statistics, economics, operations research, machine learning or computational social choice among others. It has been even more revitalized by modern applications such as recommender systems, where the goal is to infer users preferences in order to make them the best personalized suggestions. In these settings, users express their preferences only on small and varying subsets of a large catalog of items. The analysis of such incomplete rankings poses however both a great statistical and computational challenge, leading industrial actors to use methods that only exploit a fraction of available information. This thesis introduces a new representation for the data, which by construction overcomes the two aforementioned challenges. Though it relies on results from combinatorics and algebraic topology, it shares several analogies with multiresolution analysis, offering a natural and efficient framework for the analysis of incomplete rankings. As it does not involve any assumption on the data, it already leads to overperforming estimators in small-scale settings and can be combined with many regularization procedures for large-scale settings. For all those reasons, we believe that this multiresolution representation paves the way for a wide range of future developments and applications
Sibony, Eric. "Analyse mustirésolution de données de classements." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0036.
Full textThis thesis introduces a multiresolution analysis framework for ranking data. Initiated in the 18th century in the context of elections, the analysis of ranking data has attracted a major interest in many fields of the scientific literature : psychometry, statistics, economics, operations research, machine learning or computational social choice among others. It has been even more revitalized by modern applications such as recommender systems, where the goal is to infer users preferences in order to make them the best personalized suggestions. In these settings, users express their preferences only on small and varying subsets of a large catalog of items. The analysis of such incomplete rankings poses however both a great statistical and computational challenge, leading industrial actors to use methods that only exploit a fraction of available information. This thesis introduces a new representation for the data, which by construction overcomes the two aforementioned challenges. Though it relies on results from combinatorics and algebraic topology, it shares several analogies with multiresolution analysis, offering a natural and efficient framework for the analysis of incomplete rankings. As it does not involve any assumption on the data, it already leads to overperforming estimators in small-scale settings and can be combined with many regularization procedures for large-scale settings. For all those reasons, we believe that this multiresolution representation paves the way for a wide range of future developments and applications
Périnel, Emmanuel. "Segmentation en analyse de données symboliques : le cas de données probabilistes." Paris 9, 1996. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1996PA090079.
Full textBeaufils, Bertrand. "Topological Data Analysis and Statistical Learning for measuring pedestrian activities from inertial sensors." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS107.
Full textThis thesis focuses on the detection of specific movements using ActiMyo, a device developed by the company Sysnav. This system is composed by low-cost miniature inertial sensors that can be worn on the ankle and wrist. In particular, a supervised statistical learning approach aims to detect strides in ankle recordings. This first work, combined with an algorithm patented by Sysnav, allows to compute the trajectory of the pedestrian. This trajectory is then used in a new supervised learning method for the activity recognition, which is valuable information, especially in a medical context. These two algorithms offer an innovative approach based on the alignment of inertial signals and the extraction of candidate intervals which are then classified by the Gradient Boosting Trees algorithm. This thesis also presents a neural network architecture combining convolutional channels and topological data analysis for the detection of movements representative of Parkinson’s disease such as tremors and dyskinesia crises
Aaron, Catherine. "Connexité et analyse des données non linéaires." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2005. http://tel.archives-ouvertes.fr/tel-00308495.
Full textDarlay, Julien. "Analyse combinatoire de données : structures et optimisation." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00683651.
Full textOperto, Grégory. "Analyse structurelle surfacique de données fonctionnelles cétrébrales." Aix-Marseille 3, 2009. http://www.theses.fr/2009AIX30060.
Full textFunctional data acquired by magnetic resonance contain a measure of the activity in every location of the brain. If many methods exist, the automatic analysis of these data remains an open problem. In particular, the huge majority of these methods consider these data in a volume-based fashion, in the 3D acquisition space. However, most of the activity is generated within the cortex, which can be considered as a surface. Considering the data on the cortical surface has many advantages : on one hand, its geometry can be taken into account in every processing step, on the other hand considering the whole volume reduces the detection power of usually employed statistical tests. This thesis hence proposes an extension of the application field of volume-based methods to the surface-based domain by adressing problems such as projecting data onto the surface, performing surface-based multi-subjects analysis, and estimating results validity
Le, Béchec Antony. "Gestion, analyse et intégration des données transcriptomiques." Rennes 1, 2007. http://www.theses.fr/2007REN1S051.
Full textAiming at a better understanding of diseases, transcriptomic approaches allow the analysis of several thousands of genes in a single experiment. To date, international standard initiatives have allowed the utilization of large quantity of data generated using transcriptomic approaches by the whole scientific community, and a large number of algorithms are available to process and analyze the data sets. However, the major challenge remaining to tackle is now to provide biological interpretations to these large sets of data. In particular, their integration with additional biological knowledge would certainly lead to an improved understanding of complex biological mechanisms. In my thesis work, I have developed a novel and evolutive environment for the management and analysis of transcriptomic data. Micro@rray Integrated Application (M@IA) allows for management, processing and analysis of large scale expression data sets. In addition, I elaborated a computational method to combine multiple data sources and represent differentially expressed gene networks as interaction graphs. Finally, I used a meta-analysis of gene expression data extracted from the literature to select and combine similar studies associated with the progression of liver cancer. In conclusion, this work provides a novel tool and original analytical methodologies thus contributing to the emerging field of integrative biology and indispensable for a better understanding of complex pathophysiological processes
Abdali, Abdelkebir. "Systèmes experts et analyse de données industrielles." Lyon, INSA, 1992. http://www.theses.fr/1992ISAL0032.
Full textTo analyses industrial process behavio, many kinds of information are needed. As tye ar mostly numerical, statistical and data analysis methods are well-suited to this activity. Their results must be interpreted with other knowledge about analysis prcess. Our work falls within the framework of the application of the techniques of the Artificial Intelligence to the Statistics. Its aim is to study the feasibility and the development of statistical expert systems in an industrial process field. The prototype ALADIN is a knowledge-base system designed to be an intelligent assistant to help a non-specialist user analyze data collected on industrial processes, written in Turbo-Prolong, it is coupled with the statistical package MODULAD. The architecture of this system is flexible and combing knowledge with general plants, the studied process and statistical methods. Its validation is performed on continuous manufacturing processes (cement and cast iron processes). At present time, we have limited to principal Components analysis problems
David, Claire. "Analyse de XML avec données non-bornées." Paris 7, 2009. http://www.theses.fr/2009PA077107.
Full textThe motivation of the work is the specification and static analysis of schema for XML documents paying special attention to data values. We consider words and trees whose positions are labeled both by a letter from a finite alphabet and a data value from an infinite domain. Our goal is to find formalisms which offer good trade-offs between expressibility, decidability and complexity (for the satisfiability problem). We first study an extension of first-order logic with a binary predicate representing data equality. We obtain interesting some interesting results when we consider the two variable fragment. This appraoch is elegant but the complexity results are not encouraging. We proposed another formalism based data patterns which can be desired, forbidden or any boolean combination thereof. We drw precisely the decidability frontier for various fragments on this model. The complexity results that we get, while still high, seems more amenable. In terms of expressivity theses two approaches are orthogonal, the two variable fragment of the extension of FO can expressed unary key and unary foreign key while the boolean combination of data pattern can express arbitrary key but can not express foreign key
Carvalho, Francisco de. "Méthodes descriptives en analyse de données symboliques." Paris 9, 1992. https://portail.bu.dauphine.fr/fileviewer/index.php?doc=1992PA090025.
Full textRoyer, Jean-Jacques. "Analyse multivariable et filtrage des données régionalisées." Vandoeuvre-les-Nancy, INPL, 1988. http://www.theses.fr/1988NAN10312.
Full textFaye, Papa Abdoulaye. "Planification et analyse de données spatio-temporelles." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22638/document.
Full textSpatio-temporal modeling allows to make the prediction of a regionalized variable at unobserved points of a given field, based on the observations of this variable at some points of field at different times. In this thesis, we proposed a approach which combine numerical and statistical models. Indeed by using the Bayesian methods we combined the different sources of information : spatial information provided by the observations, temporal information provided by the black-box and the prior information on the phenomenon of interest. This approach allowed us to have a good prediction of the variable of interest and a good quantification of incertitude on this prediction. We also proposed a new method to construct experimental design by establishing a optimality criterion based on the uncertainty and the expected value of the phenomenon
Jamal, Sara. "Analyse spectrale des données du sondage Euclid." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0263.
Full textLarge-scale surveys, as Euclid, will produce a large set of data that will require the development of fully automated data-processing pipelines to analyze the data, extract crucial information and ensure that all requirements are met. From a survey, the redshift is an essential quantity to measure. Distinct methods to estimate redshifts exist in the literature but there is no fully-automated definition of a reliability criterion for redshift measurements. In this work, we first explored common techniques of spectral analysis, as filtering and continuum extraction, that could be used as preprocessing to improve the accuracy of spectral features measurements, then focused on developing a new methodology to automate the reliability assessment of spectroscopic redshift measurements by exploiting Machine Learning (ML) algorithms and features of the posterior redshift probability distribution function (PDF). Our idea consists in quantifying, through ML and zPDFs descriptors, the reliability of a redshift measurement into distinct partitions that describe different levels of confidence. For example, a multimodal zPDF refers to multiple (plausible) redshift solutions possibly with similar probabilities, while a strong unimodal zPDF with a low dispersion and a unique and prominent peak depicts of a more "reliable" redshift estimate. We assess that this new methodology could be very promising for next-generation large spectroscopic surveys on the ground and space such as Euclid and WFIRST
Bobin, Jérôme. "Diversité morphologique et analyse de données multivaluées." Paris 11, 2008. http://www.theses.fr/2008PA112121.
Full textLambert, Thierry. "Réalisation d'un logiciel d'analyse de données." Paris 11, 1986. http://www.theses.fr/1986PA112274.
Full textMarie, Romain. "Exploration autonome et construction de cartes topologiques référencées vision omnidirectionnelle." Amiens, 2014. https://theses.hal.science/tel-04515697.
Full textIn this work, we address the problem of autonomous exploration and topological map building in totally unknown environments for a mobile robot equipped with a sole catadioptric sensor. Multiple local representations for spatial knowledge are built upon visual information only. First, we develop an adaptated skeletonization algorithm. Applied on the extracted free space in the image, it carries the topological properties of the observed scene, and describes safe trajectories in the environment. Second, we propose a visual signature using the complement of the free space in the image, so that only the most relevant photometric information is considered. Using this representation, the robot can map the environment into a collection of places, and use them to keep track of its localization. The built representations are then organized in a topological map of the environment, which allows the robot to handle high-level behaviours (leading for instance to a structured exploration and coverage of the environment)
Fraisse, Bernard. "Automatisation, traitement du signal et recueil de données en diffraction x et analyse thermique : Exploitation, analyse et représentation des données." Montpellier 2, 1995. http://www.theses.fr/1995MON20152.
Full text