"The goal of this thesis is to extend the theory and practice of matrix completion algorithms, and how they can be utilized, improved, and scaled up to handle large data sets. Matrix completion involves predicting missing entries in real-world data matrices using the modeling assumption that the fully observed matrix is low-rank. Low-rank matrices appear across a broad selection of domains, and such a modeling assumption is similar in spirit to Principal Component Analysis. Our focus is on large scale problems, where the matrices have millions of rows and columns. In this thesis we provide new analysis for the convergence rates of matrix completion techniques using convex nuclear norm relaxation. In addition, we validate these results on both synthetic data and data from two real-world domains (recommender systems and Internet tomography). The results we obtain show that with an empirical, data-inspired understanding of various parameters in the algorithm, this matrix completion problem can be solved more efficiently than some previous theory suggests, and therefore can be extended to much larger problems with greater ease. "

How to handle large multi-dimensional datasets such as hyperspectral images and video information both efficiently and effectively plays an important role in big-data processing. The characteristics of tensor low-rank decomposition in recent years demonstrate the importance of capturing the tensor structure adequately which usually yields efficacious approaches. In this dissertation, we first aim to explore the tensor singular value decomposition (t-SVD) with the nonconvex regularization on the multi-view subspace clustering (MSC) problem, then develop two new tensor decomposition models with the Bayesian inference framework on the tensor completion and tensor robust principal component analysis (TRPCA) and tensor completion (TC) problems. Specifically, the following developments for multi-dimensional datasets under the mathematical tensor framework will be addressed. (1) By utilizing the t-SVD proposed by Kilmer et al. \cite{kilmer2013third}, we unify the Hyper-Laplacian (HL) and exclusive $\ell_{2,1}$ (L21) regularization with Tensor Log-Determinant Rank Minimization (TLD) to identify data clusters from the multiple views' inherent information. Whereby the HL regularization maintains the local geometrical structure that makes the estimation prune to nonlinearities, and the mixed $\ell_{2,1}$ and $\ell_{1,2}$ regularization provides the joint sparsity within-cluster as well as the exclusive sparsity between-cluster. Furthermore, a log-determinant function is used as a tighter tensor rank approximation to discriminate the dimension of features. (2) By considering a tube as an atom of a third-order tensor and constructing a data-driven learning dictionary from the observed noisy data along the tubes of a tensor, we develop a Bayesian dictionary learning model with tensor tubal transformed factorization to identify the underlying low-tubal-rank structure of the tensor substantially with the data-adaptive dictionary for the TRPCA problem. With the defined page-wise operators, an efficient variational Bayesian dictionary learning algorithm is established for TPRCA that enables to update of the posterior distributions along the third dimension simultaneously. (3) With the defined matrix outer product into the tensor decomposition process, we present a new decomposition model for a third-order tensor. The fundamental idea is to decompose tensors mathematically in a compact manner as much as possible. By incorporating the framework of Bayesian probabilistic inference, the new tensor decomposition model on the subtle matrix outer product (BPMOP) is developed for the TC and TRPCA problems. Extensive experiments on synthetic data and real-world datasets are conducted for the multi-view clustering, TC, and TRPCA problems to demonstrate the desirable effectiveness of the proposed approaches, by detailed comparison with currently available results in the literature.

Les exigences en matière de bruit rayonné par les navires de la Marine ou de recherche engendrent le développement de nouvelles méthodes pour améliorer leurs caractérisations. Le propulseur, qui est la source la plus importante en champ lointain, est généralement étudié en tunnel hydrodynamique. Cependant, compte tenu de la réverbération dans le tunnel et du niveau élevé du bruit de couche limite turbulente (CLT), la caractérisation peut s’ avérer délicate. L'objectif de la thèse est d'améliorer les capacités de mesures acoustiques du Grand Tunnel Hydrodynamique (GTH) de la DGA en matière de bruits émis par les maquettes testées dans des configurations d'écoulement.Un modèle de propagation basé sur la théorie des sources images est utilisé afin de prendre en compte le confinement du tunnel. Les coefficients de réflexion associés aux parois du tunnel sont identifiés par méthode inverse et à partir de la connaissance de quelques fonctions de transfert. Un algorithme de débruitage qui repose sur l’ Analyse en Composantes Principales Robuste est également proposé. Il s'agit de séparer, de manière aveugle ou semi-aveugle, l’ information acoustique du bruit de CLT en exploitant, respectivement, la propriété de rang faible et la structure parcimonieuse des matrices interspectrales du signal acoustique et du bruit. Ensuite, une technique d'imagerie basée sur la méthode des sources équivalentes est appliquée afin de localiser et quantifier des sources acoustiques corrélées ou décorrélées. Enfin, la potentialité des techniques proposées est évaluée expérimentalement dans le GTH en présence d'une source acoustique et d'un écoulement contrôlé. / The noise requirements for naval and research vessels lead to the development of new characterization methods. The propeller, which is the most important source in the far field, is usually studied in a water tunnel. However, due to the reverberation in the tunnel and the high level of flow noise, the characterization may be difficult. The aim of the thesis is to improve the measurement capabilities of the DGA Hydrodynamic tunnel (GTH) in terms of noise radiated by models in flow configurations.The propagation model is described through the image source method. Unfortunately, the reflection coefficients of the tunnel walls are generally unknown and it is proposed to estimate these parameters using an inverse method and the knowledge of some reference transfer functions. The boundary layer noise (BLN) may be stronger than the acoustic signal, therefore a Robust Principal Component Analysis is introduced in order to separate, blindly or semi-blindly, the acoustic signal from the noise. This algorithm is taking advantage of the low rank and sparse structure of the acoustic and the BLN cross-spectrum matrices. Then an acoustic imaging technique based on the equivalent source method is applied in order to localize and quantify correlated or decorrelated sources. Finally, the potentiality of the proposed techniques is evaluated experimentally in the GTH in the presence of an acoustic source and a controlled flow.

This diploma thesis deals with separation of backgroud and moving objects in video. Video can be represented as series of frames and each frame represented as low - rank structure - matrix. This thesis describe sparse representation of signals and robust principal component analysis. It also presents and implements algorithms - models for reconstruction of real video.

This diploma thesis deals with separation of backgroud and moving objects in video. Video can be represented as series of frames and each frame represented as low - rank structure - matrix. This thesis describe sparse representation of signals and robust principal component analysis. It also presents and implements algorithms - models for reconstruction of real video.

This diploma thesis deals with separation of backgroud and moving objects in video. Video can be represented as series of frames and each frame represented as low - rank structure - matrix. This thesis describe sparse representation of signals and robust principal component analysis. It also presents and implements algorithms - models for reconstruction of real video.

[pt] A tecnologia de poços inteligentes já possui muitos anos de experiência de campo. Inúmeras publicações tem descrito como o controle de fluxo remoto e os sistemas de monitoração podem diminuir o número de intervenções, o número de poços e aumentar a eficiência do gerenciamento de reservatórios. Apesar da maturidade dos equipamentos de completação o conceito de poço inteligente integrado como um elemento chave do Digital Oil Field ainda não está completmente desenvolvido. Sistemas permanentes de monitoração nesse contexto tem um papel fundamental como fonte da informação a respeito do sistema de produção real visando calibração de modelos e minimização de incerteza. Entretanto, cada sensor adicional representa aumento de complexidade e de risco operacional. Um entendimento fundamentado do que realmente é necessário, dos tipos de sensores aplicáveis e quais técnicas de análises estão disponíveis para extrair as informações necessárias são pontos chave para o sucesso do projeto de um poço inteligente. Este trabalho propõe uma nova forma de tratar os dados em tempo real de poços inteligentes através da centralização do pré-processamento dos dados. Um modelo poço inteligente numérico para temperatura em regime transiente foi desenvolvido, testado e validado com a intenção de gerar dados sintéticos. A aplicação foi escolhida sem perda de generalidade como um exemplo representativo para validação dos algorítmos de limpeza e extração de características desenvolvidos. Os resultados mostraram aumento da eficiência quando comparados com o estado da arte e um potencial para capturar a influência mútua entre os processos de produção. / [en] Intelligent Well (IW) technology has built-up several years production experience. Numerous publications have described how remote flow control and monitoring capabilities can lead to fewer interventions, a reduced well count and improved reservoir management. Despite the maturity of IW equipment, the concept of the integrated IW as a key element in the Digital Oil Field still not fully developed. Permanent monitoring systems in this framework play an important role as source of the necessary information about actual production system aiming model calibration and uncertainty minimization. However, each extra permanently installed sensor increases the well s installation complexity and operational risk. A well-founded understanding of what data is actually needed and what analysis techniques are available to extract the required information are key factors for the success of the IW project. This work proposes a new framework to real-time data analysis through centralizing pre-processing. A numeric IW transient temperature model is developed, tested and validated to generate synthetic data. It was chosen without loss off generality as a representative application to test and validate the cleansing and feature extraction algorithms developed. The results achieved are compared with the state of the art ones showing advantages regarding efficiency and potential to capture mutual influence among processes.