Search for dissertations about: "Sparse models"
Showing result 1 - 5 of 131 swedish dissertations containing the words Sparse models.
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1. Model Selection and Sparse Modeling
Abstract : Parametric signal models are used in a multitude of signal processing applications. This thesis deals with signals for which there are many candidate models, and it is not a priori known which model is the most appropriate one. READ MORE
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2. Estimation and optimal input design in sparse models
Abstract : Sparse parameter estimation is an important aspect of system identification, as it allows for reducing the order of a model, and also some models in system identification inherently exhibit sparsity in their parameters. The accuracy of the estimated sparse model depends directly on the performance of the sparse estimation methods. READ MORE
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3. 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
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4. Sparse Modeling Heuristics for Parameter Estimation - Applications in Statistical Signal Processing
Abstract : This thesis examines sparse statistical modeling on a range of applications in audio modeling, audio localizations, DNA sequencing, and spectroscopy. In the examined cases, the resulting estimation problems are computationally cumbersome, both as one often suffers from a lack of model order knowledge for this form of problems, but also due to the high dimensionality of the parameter spaces, which typically also yield optimization problems with numerous local minima. READ MORE
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5. 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