Results 231 to 240 of about 215,679 (256)
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2021
Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by McCullagh and Polson (2018). In that paper, the intuitive idea that sparsity, in a statistical framework, refers to those ''phenomena that are mostly negligible or seldom appreciably large'', has, for the first time, been given a mathematical definition.
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Despite its generic title, this thesis is about a specific notion of sparsity, the one introduced by McCullagh and Polson (2018). In that paper, the intuitive idea that sparsity, in a statistical framework, refers to those ''phenomena that are mostly negligible or seldom appreciably large'', has, for the first time, been given a mathematical definition.
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2012
This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a fuzzy notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties.
Nesetril, Jaroslav +1 more
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This is the first book devoted to the systematic study of sparse graphs and sparse finite structures. Although the notion of sparsity appears in various contexts and is a typical example of a fuzzy notion, the authors devised an unifying classification of general classes of structures. This approach is very robust and it has many remarkable properties.
Nesetril, Jaroslav +1 more
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2019
<p>In this dissertation, we explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in the presence of derived variables. Ranked sparsity arises in modeling applications when an expected disparity exists in the quality of information between different feature sets.
Ryan Andrew Peterson +6 more
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<p>In this dissertation, we explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in the presence of derived variables. Ranked sparsity arises in modeling applications when an expected disparity exists in the quality of information between different feature sets.
Ryan Andrew Peterson +6 more
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2011
Today, sparsity techniques have been widely used to address practical problems in the fields of medical imaging, machine learning, computer vision, data mining, compressive sensing, image processing, video analysis and multimedia. We will briefly introduce the related sparsity techniques and their successful applications on compressive sensing, sparse
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Today, sparsity techniques have been widely used to address practical problems in the fields of medical imaging, machine learning, computer vision, data mining, compressive sensing, image processing, video analysis and multimedia. We will briefly introduce the related sparsity techniques and their successful applications on compressive sensing, sparse
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Superresolution via Sparsity Constraints
SIAM Journal on Mathematical Analysis, 1992The problem of recovering a measure \(\mu\) supported on a lattice of span \(\Delta\) from measurements \(\hat\mu(\omega)\) at frequencies \(|\omega|\leq\Omega\) is considered. The author shows that even if \(\Omega\) is much smaller than the Nyquist frequency \(\pi/\Delta\) and the measurements are noisy, stable recovery is possible if the measure ...
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