Search for dissertations about: "Block sparsity"
Showing result 1 - 5 of 6 swedish dissertations containing the words Block sparsity.
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1. Enhanced block sparse signal recovery and bayesian hierarchical models with applications
Abstract : This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). READ MORE
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2. 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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3. 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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4. Network models with applications to genomic data: generalization, validation and uncertainty assessment
Abstract : The aim of this thesis is to provide a framework for the estimation and analysis of transcription networks in human cancer. The methods we develop are applied to data collected by The Cancer Genome Atlas (TCGA) and supporting simulations are based on derived models in order to reflect real data structure. READ MORE
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5. Classification models for high-dimensional data with sparsity patterns
Abstract : Today's high-throughput data collection devices, e.g. spectrometers and gene chips, create information in abundance. However, this poses serious statistical challenges, as the number of features is usually much larger than the number of observed units. READ MORE