Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"sparse approximation"”.
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Sparse Approximation In Banach Spaces
<p>The sparse approximation problems ask for complete recovery of functions in a given space that are supported by few of the elements of a system of generators for the space or for approximate recovery that involves a limited number of generators.</p> <p>Traditionally, these problems have been …
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Improvements in magnetic resonance imaging excitation pulse design
… single-output (MSSO) simultaneous sparse approximation problem. The contributions are both conceptual and algorithmic and are validated with simulations, as well as anthropogenic-object-based and in vivo trials on MRI scanners. Excitation pulses are essential to MRI: they excite …
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Gaussian processes:iterative sparse approximations
… a two-step solution to construct a probabilistic approximation to the posterior. In the first step we adapt the Bayesian online learning to GPs: the final approximation to the posterior is the result of propagating the first and second moments of intermediate posteriors obtained by combining a new …
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Dimension reduction algorithms for near-optimal low-dimensional embeddings and compressive sensing
… of datasets containing points that are sparse in the pixel basis, with the goal of recoving a nearly-optimal sparse approximation to the original data. We present several algorithms that achieve strong recovery guarantees using the near-optimal bound of measurements, while also being …
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Transfer learning algorithms for image classification
… the total number of features involved in the approximation. To solve the joint sparse approximation problem we develop an optimization algorithm whose time and memory complexity is O(n log n) with n being the number of parameters of the joint model. We conduct experiments on news-topic and …
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Sparse Bayesian information filters for localization and mapping
… information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. …
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New contributions in overcomplete image representations inspired from the functional architecture of the primary visual cortex = Nuevas contribuciones en representaciones sobrecompletas de imágenes inspiradas por la arquitectura funcional de la corteza visual primaria
… problems, in particular the problem of sparse approximation. The thesis proposes first a self-invertible log-Gabor wavelet transformation inspired from the receptive field and multiresolution arrangement of the simple cells in the primary visual cortex (V1). This transform shows …
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Task-specific and interpretable feature learning
… dissertation starts by showing how the classical sparse coding models could be improved in a task-specific way, by formulating the entire pipeline as bi-level optimization. Then, it mainly illustrates how to incorporate the structure of classical learning models, e.g., sparse coding, into the …
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Sparse seismic signal processing using adaptive dictionaries
… both stress the need for faster algorithms and sparse regularization techniques to accelerate and improve imaging results. This thesis presents a new reconstruction method to mitigate noise and interpolate missing traces in the acquired seismic dataset, as well as a new FWI framework to estimate …
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Sparse Bayesian information filters for localization and mapping
… information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. …
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Simulation-based Design with Polynomial Ridge Approximations
… explore the use of orthogonal polynomial ridge approximations to construct surrogate models. These models enable rapid evaluations and facilitate a deeper, physically-intuitive understanding of the quantities of interest. For modelling smoothly varying functions, the use of orthogonal …
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Bayesian approaches to time-frequency inverse problems
… expectation–maximisation algorithm for sparse reconstruction of corrupted audio signals. Chapters 5 and 6 mark a departure from the sparse approximation paradigm, investigating instead the potential of low-rank latent structures in time-frequency inverse problems. Following a …
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Matrix probing, skeleton decompositions, and sparse Fourier transform
… that help to solve matrices, compute low rank approximations and perform the Fast Fourier Transform. Matrix probing and its conditioning When a matrix A with n columns is known to be well approximated by a linear combination of basis matrices B1,... , Bp, we can apply A to a random vector and …
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Nonparametric choice modeling : applications to operations management
… logit (MNL) model. In addition, most of the approximation schemes proposed in the literature are tailored to a specific parametric structure. We deviate from this and propose a general algorithm to find the optimal assortment assuming access to only a subroutine that gives revenue …
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Parallelisation of greedy algorithms for compressive sensing reconstruction
Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as it is captured. The process of capturing and simultaneously compressing the signal is represented as linear sampling, which can encompass a variety of physical processes or signal processing. Instead …
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From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming
… approach) while the second one is a ""sparse optimization"" based approach. Specifically, in the first approach we combine the image key point features with the segment features together to discover the common object, while relying on the local topology consistency of both key point and …