Literatura académica sobre el tema "Factorisation en matrice non négative"
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Artículos de revistas sobre el tema "Factorisation en matrice non négative"
Dubreuil, Lisette y Pierre Ouellette. "Un test des propriétés de courbure de la demande agrégée : le Canada vs le Québec". Articles 70, n.º 3 (23 de marzo de 2009): 271–88. http://dx.doi.org/10.7202/602146ar.
Texto completoDelmaire, Gilles, Gilles Roussel, Dany Hleis y Frédéric Ledoux. "Une version pondérée de la factorisation matricielle non négative pour l'identification de sources de particules atmosphériques. Application au littoral de la mer du Nord". Journal Européen des Systèmes Automatisés 44, n.º 4-5 (30 de mayo de 2010): 547–66. http://dx.doi.org/10.3166/jesa.44.547-566.
Texto completoClearman, Sam, Brittany Shelton y Mark Skandera. "Path tableaux and combinatorial interpretations of immanants for class functions on $S_n$". Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AO,..., Proceedings (1 de enero de 2011). http://dx.doi.org/10.46298/dmtcs.2906.
Texto completoKarp, Steven N. "Sign variation, the Grassmannian, and total positivity". Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings, 27th..., Proceedings (1 de enero de 2015). http://dx.doi.org/10.46298/dmtcs.2518.
Texto completoLaplantine, François. "Wu Wei". Anthropen, 2016. http://dx.doi.org/10.17184/eac.anthropen.0029.
Texto completoTesis sobre el tema "Factorisation en matrice non négative"
Limem, Abdelhakim. "Méthodes informées de factorisation matricielle non négative : Application à l'identification de sources de particules industrielles". Thesis, Littoral, 2014. http://www.theses.fr/2014DUNK0432/document.
Texto completoNMF methods aim to factorize a non negative observation matrix X as the product X = G.F between two non-negative matrices G and F. Although these approaches have been studied with great interest in the scientific community, they often suffer from a lack of robustness to data and to initial conditions, and provide multiple solutions. To this end and in order to reduce the space of admissible solutions, the work proposed in this thesis aims to inform NMF, thus placing our work in between regression and classic blind factorization. In addition, some cost functions called parametric αβ-divergences are used, so that the resulting NMF methods are robust to outliers in the data. Three types of constraints are introduced on the matrix F, i. e., (i) the "exact" or "bounded" knowledge on some components, and (ii) the sum to 1 of each line of F. Update rules are proposed so that all these constraints are taken into account by mixing multiplicative methods with projection. Moreover, we propose to constrain the structure of the matrix G by the use of a physical model, in order to discern sources which are influent at the receiver. The considered application - consisting of source identification of particulate matter in the air around an insdustrial area on the French northern coast - showed the interest of the proposed methods. Through a series of experiments on both synthetic and real data, we show the contribution of different informations to make the factorization results more consistent in terms of physical interpretation and less dependent of the initialization
Dia, Nafissa. "Suivi non-invasif du rythme cardiaque foetal : exploitation de la factorisation non-négative des matrices sur signaux électrocardiographiques et phonocardiographiques". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAS034.
Texto completoWith more than 200,000 births per day in the world, fetal well-being monitoring during birth is a major clinical challenge. This monitoring is done by analyzing the fetal heart rate (FHR) and its variability, and this has to be robust while minimizing the number of non-invasive sensors to lay on the mother's abdomen.In this context, electrocardiogram (ECG) and phonocardiogram (PCG) signals are of interest since they both bring cardiac information, both redundant and complementary. This multimodality as well as some features of ECG and PCG signals, as quasi-periodicity, have been exploited. Several propositions were put in competition, based on non-negative matrix factorization (NMF), a matrix decomposition algorithm adapted to physiological signals.The final solution proposed for the FHR estimation is based on a source-filter modeling of real fetal ECG or PCG signals, previously extracted, allowing an estimation of the fundamental frequency by NMF.The approach was carried out on a clinical database of ECG and PCG signals on pregnant women and FHR results were validated by comparison with the cardiotocography clinical reference technique
Chreiky, Robert. "Informed Non-Negative Matrix Factorization for Source Apportionment". Thesis, Littoral, 2017. http://www.theses.fr/2017DUNK0464/document.
Texto completoSource apportionment for air pollution may be formulated as a NMF problem by decomposing the data matrix X into a matrix product of two factors G and F, respectively the contribution matrix and the profile matrix. Usually, chemical data are corrupted with a significant proportion of abnormal data. Despite the interest for the community for NMF methods, they suffer from a lack of robustness to a few abnormal data and to initial conditions and they generally provide multiple minima. To this end, this thesis is oriented on one hand towards robust NMF methods and on the other hand on informed NMF by using some specific prior knowledge. Two types of knowlodge are introduced on the profile matrix F. The first assumption is the exact knowledge on some of flexible components of matrix F and the second hypothesis is the sum-to-1 constraint on each row of the matrix F. A parametrization able to deal with both information is developed and update rules are proposed in the space of constraints at each iteration. These formulations have been appliede to two kind of robust cost functions, namely, the weighted Huber cost function and the weighted αβ divergence. The target application-namely, identify the sources of particulate matter in the air in the coastal area of northern France - shows relevance of the proposed methods. In the numerous experiments conducted on both synthetic and real data, the effect and the relevance of the different information is highlighted to make the factorization results more reliable
Ravel, Sylvain. "Démixage d’images hyperspectrales en présence d’objets de petite taille". Thesis, Ecole centrale de Marseille, 2017. http://www.theses.fr/2017ECDM0006/document.
Texto completoThis thesis is devoted to the unmixing issue in hyperspectral images, especiallyin presence of small sized objects. Hyperspectral images contains an importantamount of both spectral and spatial information. Each pixel of the image canbe assimilated to the reflection spectra of the imaged scene. Due to sensors’ lowspatial resolution, the observed spectra are a mixture of the reflection spectraof the different materials present in the pixel. The unmixing issue consists inestimating those materials’ spectra, called endmembers, and their correspondingabundances in each pixel. Numerous unmixing methods have been proposed butthey fail when an endmembers is rare (that is to say an endmember present inonly a few of the pixels). We call rare pixels, pixels containing those endmembers.The presence of those rare endmembers can be seen as anomalies that we want todetect and unmix. In a first time, we present two detection methods to retrievethis anomalies. The first one use a thresholding criterion on the reconstructionerror from estimated dominant endmembers. The second one, is based on wavelettransform. Then we propose an unmixing method adapted when some endmembersare known a priori. This method is then used with the presented detectionmethod to propose an algorithm to unmix the rare pixels’ endmembers. Finally,we study the application of bootstrap resampling method to artificially upsamplerare pixels and propose unmixing methods in presence of small sized targets
Brisebarre, Godefroy. "Détection de changements en imagerie hyperspectrale : une approche directionnelle". Thesis, Ecole centrale de Marseille, 2014. http://www.theses.fr/2014ECDM0010.
Texto completoHyperspectral imagery is an emerging imagery technology which has known a growing interest since the 2000’s. This technology allows an impressive growth of the data registered from a specific scene compared to classical RGB imagery. Indeed, although the spatial resolution is significantly lower, the spectral resolution is very small and the covered spectral area is very wide. We focus on change detection between two images of a given scene for defense oriented purposes.In the following, we start by introducing hyperspectral imagery and the specificity of its exploitation for defence purposes. We then present a change detection and analysis method based on the search for specifical directions in the space generated by the image couple, followed by a merging of the nearby directions. We then exploit this information focusing on theunmixing capabilities of multitemporal hyperspectral data. Finally, we will present a range of further works that could be done in relation with our work and conclude about it
Hubert, Xavier. "Etude de faisabilité de l'estimation non-invasive de la fonction d'entrée artérielle B+ pour l'imagerie TEP chez l'homme". Phd thesis, Ecole Centrale Paris, 2009. http://tel.archives-ouvertes.fr/tel-00536849.
Texto completoRigaud, François. "Modèles de signaux musicaux informés par la physiques des instruments : Application à l'analyse automatique de musique pour piano par factorisation en matrices non-négatives". Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0073/document.
Texto completoThis thesis introduces new models of music signals informed by the physics of the instruments. While instrumental acoustics and audio signal processing target the modeling of musical tones from different perspectives (modeling of the production mechanism of the sound vs modeling of the generic "morphological'' features of the sound), this thesis aims at mixing both approaches by constraining generic signal models with acoustics-based information. Thus, it is here intended to design instrument-specific models for applications both to acoustics (learning of parameters related to the design and the tuning) and signal processing (transcription). In particular, we focus on piano music analysis for which the tones have the well-known property of inharmonicity. The inclusion of such a property in signal models however makes the optimization harder, and may even damage the performance in tasks such as music transcription when compared to a simpler harmonic model. A major goal of this thesis is thus to have a better understanding about the issues arising from the explicit inclusion of the inharmonicity in signal models, and to investigate whether it is really valuable when targeting tasks such as polyphonic music transcription
Dereure, Erwan. "Quantitative analysis of bioluminescent signals in preclinical imaging". Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS090.
Texto completoBioluminescence imaging (BLI) is an optical imaging technology in which a living organism or cell emits light through a biological substrate/enzyme reaction without any light excitation.This technology, used in preclinical oncology in order to quantify the tumor status in a non-invasive way, is still quite recent and for now biologists lack automated processing tools to improve the quantification of images. In addition, some experimental protocols require to extract the photon flux of multiple tumors on the same side of the animal. This can be difficult and can introduce errors and biases as BLI suffers from a lack of robustness because of a variability in vascularization, or hypoxic and necrotic zones within the tumors. In this work, we propose the use of Non-Negative Matrix Factorization to separate the photon flux of different tumors within the same bioluminescence image by leveraging the different pixel-wise temporal patterns. Such spatio-temporal unmixing yields several important challenges that we have tackled. In a first contribution, we use prior knowledge on the appearance of the tumors and show the importance of penalizing the norm of the wavelet coefficients corresponding to the sources estimated during the optimization process to obtain a high spatial consistency of unmixed tumors. In a second contribution we deal with strong heterogeneities within tumors corrupting the separation by presenting a dedicated pipeline for pre-aligning the photon flux of the different pixels. We show that the resulting method is capable of accurately extracting the photon flux of different tumors present within a single bioluminescence image. These algorithms were tested and validated on two real BLI datasets and on one synthetic dataset generated with a bioluminescence image simulator we designed and developed. In a third contribution, we propose a pharmacokinetics model to calibrate the tumor photon flux based on the bioluminescence signal emitted by a muscle. This allows us to extract meaningful physiological parameters from the image like substrate exchange rates. We show that these parameters represent significant features of the tumor state and can be used to improve the quantification of bioluminescence images
Rigaud, François. "Modèles de signaux musicaux informés par la physiques des instruments : Application à l'analyse automatique de musique pour piano par factorisation en matrices non-négatives". Electronic Thesis or Diss., Paris, ENST, 2013. http://www.theses.fr/2013ENST0073.
Texto completoThis thesis introduces new models of music signals informed by the physics of the instruments. While instrumental acoustics and audio signal processing target the modeling of musical tones from different perspectives (modeling of the production mechanism of the sound vs modeling of the generic "morphological'' features of the sound), this thesis aims at mixing both approaches by constraining generic signal models with acoustics-based information. Thus, it is here intended to design instrument-specific models for applications both to acoustics (learning of parameters related to the design and the tuning) and signal processing (transcription). In particular, we focus on piano music analysis for which the tones have the well-known property of inharmonicity. The inclusion of such a property in signal models however makes the optimization harder, and may even damage the performance in tasks such as music transcription when compared to a simpler harmonic model. A major goal of this thesis is thus to have a better understanding about the issues arising from the explicit inclusion of the inharmonicity in signal models, and to investigate whether it is really valuable when targeting tasks such as polyphonic music transcription
Meganem, Inès. "Méthodes de séparation aveugle de sources pour l'imagerie hyperspectrale : application à la télédétection urbaine et à l'astrophysique". Phd thesis, Toulouse 3, 2012. http://thesesups.ups-tlse.fr/1790/.
Texto completoIn this work, we developed Blind Source Separation methods (BSS) for hyperspectral images, concerning two applications : urban remote sensing and astrophysics. The first part of this work concerned spectral unmixing for urban images, with the aim of finding, by an unsupervised method, the materials present in the scene, by extracting their spectra and their proportions. Most existing methods rely on a linear model, which is not valid in urban environments because of 3D structures. Therefore, the first step was to derive a mixing model adapted to urban environments, starting from physical equations based on radiative transfer theory. The derived linear-quadratic model, and possible hypotheses on the mixing coefficients, are justified by results obtained with simulated realistic images. We then proposed, for the unmixing, BSS methods based on NMF (Non-negative Matrix Factorization). These methods are based on gradient computation taking into account the quadratic terms. The first method uses a gradient descent algorithm with a constant step, from which we then derived a Newton version. The last proposed method is a multiplicative NMF algorithm. These methods give better performance than a linear method from the literature. Concerning astrophysics, we developed BSS methods for dense field images of the MUSE instrument. Due to the PSF (Point Spread Function) effect, information contained in the pixels can result from contributions of many stars. Hence, there is a need for BSS, to extract from these signals that are mixtures, the star spectra which are our "sources". The mixing model is linear but spectrally non-invariant. We proposed a BSS method based on positivity. This approach uses the parametric model of MUSE FSF (Field Spread Function). The implemented method is iterative and alternates spectra estimation using least squares (with positivity constraint) and FSF parameter estimation by a projected gradient descent algorithm. The proposed method yields good performance with simulated MUSE images