Relevant bibliographies by topics / Low-Rank Tensor

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Academic literature on the topic 'Low-Rank Tensor'

Author: Grafiati
Published: 27 January 2024

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Dissertations / Theses on the topic "Low-Rank Tensor"

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Stojanac, Željka [Verfasser]. "Low-rank Tensor Recovery / Željka Stojanac." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1119888565/34.

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Shi, Qiquan. "Low rank tensor decomposition for feature extraction and tensor recovery." HKBU Institutional Repository, 2018. https://repository.hkbu.edu.hk/etd_oa/549.

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Feature extraction and tensor recovery problems are important yet challenging, particularly for multi-dimensional data with missing values and/or noise. Low-rank tensor decomposition approaches are widely used for solving these problems. This thesis focuses on three common tensor decompositions (CP, Tucker and t-SVD) and develops a set of decomposition-based approaches. The proposed methods aim to extract low-dimensional features from complete/incomplete data and recover tensors given partial and/or grossly corrupted observations.;Based on CP decomposition, semi-orthogonal multilinear principa
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Han, Xu. "Robust low-rank tensor approximations using group sparsity." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S001/document.

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Le développement de méthodes de décomposition de tableaux multi-dimensionnels suscite toujours autant d'attention, notamment d'un point de vue applicatif. La plupart des algorithmes, de décompositions tensorielles, existants requièrent une estimation du rang du tenseur et sont sensibles à une surestimation de ce dernier. Toutefois, une telle estimation peut être difficile par exemple pour des rapports signal à bruit faibles. D'un autre côté, estimer simultanément le rang et les matrices de facteurs du tenseur ou du tenseur cœur n'est pas tâche facile tant les problèmes de minimisation de rang
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Benedikt, Udo. "Low-Rank Tensor Approximation in post Hartree-Fock Methods." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-133194.

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In this thesis the application of novel tensor decomposition and tensor representation techniques in highly accurate post Hartree-Fock methods is evaluated. These representation techniques can help to overcome the steep scaling behaviour of high level ab-initio calculations with increasing system size and therefore break the "curse of dimensionality". After a comparison of various tensor formats the application of the "canonical polyadic" format (CP) is described in detail. There, especially the casting of a normal, index based tensor into the CP format (tensor decomposition) and a method for
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Rabusseau, Guillaume. "A tensor perspective on weighted automata, low-rank regression and algebraic mixtures." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4062.

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Ce manuscrit regroupe différents travaux explorant les interactions entre les tenseurs et l'apprentissage automatique. Le premier chapitre est consacré à l'extension des modèles de séries reconnaissables de chaînes et d'arbres aux graphes. Nous y montrons que les modèles d'automates pondérés de chaînes et d'arbres peuvent être interprétés d'une manière simple et unifiée à l'aide de réseaux de tenseurs, et que cette interprétation s'étend naturellement aux graphes ; nous étudions certaines propriétés de ce modèle et présentons des résultats préliminaires sur leur apprentissage. Le second chapit
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Alora, John Irvin P. "Automated synthesis of low-rank stochastic dynamical systems using the tensor-train decomposition." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105006.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 79-83).<br>Cyber-physical systems are increasingly becoming integrated in various fields such as medicine, finance, robotics, and energy. In these systems and their applications, safety and correctness of operation is of primary concern, spar
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Ceruti, Gianluca [Verfasser]. "Unconventional contributions to dynamical low-rank approximation of tree tensor networks / Gianluca Ceruti." Tübingen : Universitätsbibliothek Tübingen, 2021. http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1186805.

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Gorodetsky, Alex Arkady. "Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108918.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 205-214).<br>Optimal decision making under uncertainty is critical for control and optimization of complex systems. However, many techniques for solving problems such as stochastic optimal control and data assimilation encounter the curse of dimensionality when too many state variables are involved. In this thesis, we propose a framework for computing with high-dimensional functions that mitigates this ex
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Benedikt, Udo [Verfasser], Alexander A. [Akademischer Betreuer] Auer, and Sibylle [Gutachter] Gemming. "Low-Rank Tensor Approximation in post Hartree-Fock Methods / Udo Benedikt ; Gutachter: Sibylle Gemming ; Betreuer: Alexander A. Auer." Chemnitz : Universitätsbibliothek Chemnitz, 2014. http://d-nb.info/1230577440/34.

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10

Cordolino, Sobral Andrews. "Robust low-rank and sparse decomposition for moving object detection : from matrices to tensors." Thesis, La Rochelle, 2017. http://www.theses.fr/2017LAROS007/document.

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Dans ce manuscrit de thèse, nous introduisons les avancées récentes sur la décomposition en matrices (et tenseurs) de rang faible et parcimonieuse ainsi que les contributions pour faire face aux principaux problèmes dans ce domaine. Nous présentons d’abord un aperçu des méthodes matricielles et tensorielles les plus récentes ainsi que ses applications sur la modélisation d’arrière-plan et la segmentation du premier plan. Ensuite, nous abordons le problème de l’initialisation du modèle de fond comme un processus de reconstruction à partir de données manquantes ou corrompues. Une nouvelle méthod
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