IMPROVED ANALYSIS OF THE SUBSAMPLED RANDOMIZED HADAMARD TRANSFORM
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Publication:3101550
DOI10.1142/S1793536911000787zbMath1232.15029arXiv1011.1595OpenAlexW2963455674MaRDI QIDQ3101550
Publication date: 29 November 2011
Published in: Advances in Adaptive Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1011.1595
Random matrices (algebraic aspects) (15B52) Linear transformations, semilinear transformations (15A04) Boolean and Hadamard matrices (15B34)
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Cites Work
- Unnamed Item
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- On the conditioning of random subdictionaries
- A fast randomized algorithm for the approximation of matrices
- On Talagrand's deviation inequalities for product measures
- Extensions of Lipschitz mappings into a Hilbert space