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A fast randomized algorithm for overdetermined linear least-squares regression
We introduce a randomized algorithm for overdetermined linear least-squares regression. Given an arbitrary full-rank m × n matrix A with m ≥ n, any m × 1 vector b, and any positive real number ε, the procedure computes an n × 1 vector x such that x minimizes the Euclidean norm ‖Ax − b‖ to relative p...
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| Autori principali: | , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
National Academy of Sciences
2008
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2734343/ https://ncbi.nlm.nih.gov/pubmed/18779559 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.0804869105 |
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