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 6 of 6 for “"low-rank model"”.
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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data
… been difficult to find general constructions for models in which efficient exact inference is possible, outside of certain classical cases. We identify a class of such models that are tractable owing to a certain "low-rank" structure in the potentials that couple neighboring variables. In the …
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Low-rank estimation and embedding learning: theory and applications
… feature space. For example, in the vector space model of text data, the feature dimension is the vocabulary size. If representing a social network using an adjacency matrix, the feature dimension corresponds to the number of objects in the network. Many other datasets also fall into this …
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Adaptive nonlocal and structured sparse signal modeling and applications
… especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. Many applications such as image and video denoising, inpainting, demosaicing, super-resolution, magnetic resonance imaging (MRI), and computed tomography (CT) …
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Non-Parametric Spatial Models
… I undertake two non-parametric approaches to modelling the covariance functions.</p> <p>Our approach is motivated by problems that arise in spatial data analysis in recent years. First, it is nontrivial to choose a parametric family among many parametric families of covariance function. A …
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A subspace approach to high-resolution magnetic resonance spectroscopic imaging
… of in vivo MRSI have been progressing more slowly than expected. The main reasons for this situation are the problems of long data acquisition time, poor spatial resolution and low signal-to-noise ratio (SNR) for this imaging modality. In the last four decades, significant efforts have been …
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Fast MRI with sparse sampling: models, algorithms, and applications
… imaging approaches, including imaging models and reconstruction algorithms, to enable high-quality reconstruction from highly undersampled data. The utility of the proposed techniques is demonstrated in two challenging higher-dimensional MRI applications, i.e., dynamic MRI and MR …