Improved matrix algorithms via the subsampled randomized Hadamard transform (Q2866237)
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scientific article; zbMATH DE number 6238087
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Improved matrix algorithms via the subsampled randomized Hadamard transform |
scientific article; zbMATH DE number 6238087 |
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13 December 2013
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low-rank approximation
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randomized algorithm
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Hadamard transform
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least squares regression
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Improved matrix algorithms via the subsampled randomized Hadamard transform (English)
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Several recent randomized linear algebra algorithms rely upon fast dimension reduction methods. A popular choice is the subsampled randomized Hadamard transform (SRHT). The authors address the efficacy of an SRHT-based low-rank matrix approximation technique introduced by \textit{F. Woolfe} et al. [Appl. Comput. Harmon. Anal. 25, No. 3, 335--366 (2008; Zbl 1155.65035)]. Some sharper approximation bounds are proved. They also discuss two approaches to least squares regression involving SRHT dimensionality reduction.
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