Search for dissertations about: "sparsity"
Showing result 6 - 10 of 78 swedish dissertations containing the word sparsity.
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6. The Quest for Robust Model Selection Methods in Linear Regression
Abstract : A fundamental requirement in data analysis is fitting the data to a model that can be used for the purpose of prediction and knowledge discovery. A typical and favored approach is using a linear model that explains the relationship between the response and the independent variables. READ MORE
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7. Sparse Modeling of Grouped Line Spectra
Abstract : 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 thesis exploits sparse modeling, where the orders are implicitly chosen. READ MORE
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8. Parameter Estimation - in sparsity we trust
Abstract : This thesis is based on nine papers, all concerned with parameter estimation. The thesis aims at solving problems related to real-world applications such as spectroscopy, DNA sequencing, and audio processing, using sparse modeling heuristics. READ MORE
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9. Parameter Estimation and Filtering Using Sparse Modeling
Abstract : Sparsity-based estimation techniques deal with the problem of retrieving a data vector from an undercomplete set of linear observations, when the data vector is known to have few nonzero elements with unknown positions. It is also known as the atomic decomposition problem, and has been carefully studied in the field of compressed sensing. READ MORE
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10. Efficient Structure and Motion: Path Planning, Uncertainty and Sparsity
Abstract : This thesis explores methods for solving the structure-and-motion problem in computer vision, the recovery of three-dimensional data from a series of two-dimensional image projections. The first paper investigates an alternative state space parametrization for use with the Kalman filter approach to simultaneous localization and mapping, and shows it has superior convergence properties compared with the state-of-the-art. READ MORE