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 “"block-sparsity"”.
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Bayesian and Information-Theoretic Learning of High Dimensional Data
… which is closely related to the concept of block sparsity. </p><p>Finally in the information-theoretic projection design, a linear projection matrix is explicitly sought for information-preserving dimensionality reduction. All the methods mentioned above prove to be effective in learning …
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Adaptive OFDM Radar for Target Detection and Tracking
… a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the $ell_1$-constrained minimal singular …
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Computable Performance Analysis of Recovering Signals with Low-dimensional Structures
… low-dimensional structures, particularly, the sparsity, the block-sparsity, the low-rankness, and the low-dimensional manifold structures of general nonlinear data sets. The reconstruction performance of these signals relies heavily on the structure of the sensing matrix/operator. In many …
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Sparse spectrum fitting in array processing
In this thesis, we focus on the application of sparsity-encouraging regularization techniques to the problem of Direction-Of-Arrival (DOA) estimation using sensor arrays. By developing the sparse representation models for the spatial covariance matrix of correlated or uncorrelated sources …
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Explicitly Correlated Methods for Large Molecular Systems
… the use of domain approximation to impose the block sparsity. This development utilizes the concepts for sparse representation of tensors described in the context of the DLPNO-MP2 method by Neese, Valeev and co-workers [Pinski et al., {em J. Chem. Phys.} {bf 143}, 034108 (2015)]. Novel …
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Sparse Modeling of Grouped Line Spectra
This licentiate thesis focuses on clustered parametric models for estimation of line spectra, when the spectral content of a signal source is assumed to exhibit some form of grouping. Different from previous parametric approaches, which generally require explicit knowledge of the model orders, this …