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 20 of 36 for “"Dictionary learning"”.
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Dictionary learning for scalable sparse image representation
… basis vectors i.e., atoms given an overcomplete dictionary. Applications that employ sparse representation are many such as denoising, compression, and regularisation in inverse problems, feature extraction, and more. In this thesis we introduce and study a particular signal representation …
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Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… nonnegative increments. In particular, we study dictionary learning for sparse image representation using the beta process and the dependent hierarchical beta process, and we present the negative binomial process, a novel nonparametric Bayesian prior that unites the seemingly disjoint problems of …
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On the Local Correctness of L1-minimization for Dictionary Learning Algorithm
Item withdrawn by Rebecca Bryant (rabryant@illinois.edu) on 2011-12-01T20:45:07Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 GENG_QUAN.pdf: 460059 bytes, checksum: 9fe702d7efe57d2efd8da6d4af67f05b (MD5)
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Adversarial Déjà Vu: Jailbreak Dictionary Learning for Stronger Generalization to Unseen Attacks
… compress these skills into a compact Jailbreak Dictionary using sparse dictionary learning, yielding a set of interpretable adversarial skill primitives. Through temporal cutoff experiments, we demonstrate that attacks released after a given cutoff can be effectively explained as sparse …
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Magnetic resonance image reconstruction from highly undersampled K-Space data using dictionary learning
… we propose a novel framework for adaptively learning the sparsifying transform (dictionary), and reconstructing the image simultaneously from highly undersampled k-space data. The sparsity in this framework is enforced on overlapping image patches emphasizing local structure. Moreover, the …
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Ambient seismic noise tomography of the southern United States and seismic inversion with dictionary learning using unsupervised machine learning.
… structures, we incorporate unsupervised machine learning. CNN and U-Net decompose seismic traces into dictionary and coefficients, reconstruct reflectivity, and convolve it with a wavelet. Lasso regularization aids training. We also use Variational Autoencoders (VAEs) for efficient high-frequency …
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Deconvolution and sparsity based image restoration
… deconvolution for blurred image restoration and dictionary learning algorithms for sparse image denoising and in-painting. In the first approach, iterative least square and maximum likelihood based deconvolution methods are derived for image deblurring application. Three novel methods are …
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Deconvolution and sparsity based image restoration
… deconvolution for blurred image restoration and dictionary learning algorithms for sparse image denoising and in-painting. In the first approach, iterative least square and maximum likelihood based deconvolution methods are derived for image deblurring application. Three novel methods are …
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Enhancing Generalization in Sketch-Based Image Retrieval through Single and Multi-Source Domain Adaptation
… canonical correlation analysis (CCA) alongside dictionary learning principles and sparse optimization techniques to facili- tate effective knowledge transfer from a source (e.g., images) to a target domain (e.g., sketches), even in few-shot scenarios . This approach is further extended to a …
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Non-asymptotic bounds for prediction problems and density estimation.
This dissertation investigates the learning scenarios where a high-dimensional parameter has to be estimated from a given sample of fixed size, often smaller than the dimension of the problem. The first part answers some open questions for the binary classification problem in the framework of …
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Compressive Sensing in Positron Emission Tomography (PET) Imaging
… a different CS model based on an adaptive dictionary learning (DL) technique for data recovery in PET imaging was developed. Specifically, a PET image is decomposed into small overlapped patches and the dictionary is learned from these overlapped patches. The technique has good sparsifying …
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Adaptive nonlocal and structured sparse signal modeling and applications
… representation, 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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A Study of Dimensionality Reduction Techniques and its Analysis on Climate Data
… techniques explored are Factor Analysis and Dictionary Learning. In many problems, the observations are high-dimensional but we may have reason to believe that the they lie near a lower-dimensional manifold. In other words, we may believe that high-dimensional data are multiple, indirect …
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Tomographic reconstruction with adaptive sparsifying transforms
… representations. In particular, the synthesis dictionary learning framework has been shown to outperform traditional regularization techniques. However, these methods scale poorly with data size, and may be prohibitively expensive for practical tomographic reconstruction. In this thesis, we …
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A Unified Robust Minimax Framework for Regularized Learning Problems
… to apply minimax related concepts to real-world learning tasks, we develop a new fault-tolerant classification framework to combat class noise for general multi-class classification problems; further, by studying the relationship between the majorizable function class and the minimax framework, …
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Inverse Constitutional AI
… prompt optimization approaches, and sparse dictionary learning methods. In this work, I argue the following thesis: ICAI shows promise as a strategy to disentangle and explain the preferences represented in preference data. A clustering-based approach to ICAI, though, fails to successfully …
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Acquisitions d'IRM de diffusion à haute résolution spatiale : nouvelles perspectives grâce au débruitage spatialement adaptatif et angulaire
… base sur les principes du block matching et du dictionary learning pour exploiter la redondance des données d’IRM de diffusion. Un seuillage sur les voisins angulaire est aussi réalisé à l’aide du sparse coding, où l’erreur de reconstruction en norme l2 est bornée par la variance locale du …
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ON RICCATI EQUATIONS IN NONCONVEX OPTIMIZATION
… problems such as low-rank matrix recovery, dictionary learning, and certain formulations of optimal control, which have optimization landscapes that are well-behaved in the sense where every local minimum is global and critical points are connected through predictable symmetries. These …
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Challenges in recommender systems : scalability, privacy, and structured recommendations
… to non-negative matrix factorization (NMF) and dictionary learning for sparse coding. Privacy is another important issue in RS. Indeed, there is an inherent trade-off between accuracy of recommendations and the extent to which users are willing to release information about their preferences. We …
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