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Poster
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Charlie Dickens · Graham Cormode · David Woodruff
Work on approximate linear algebra has led to efficient distributed and streaming algorithms for problems such as approximate matrix multiplication, low rank approximation, and regression, primarily for the Euclidean norm $\ell_2$. We study other $\ell_p$ norms, which are more robust for $p < 2$, and can be used to find outliers for $p > 2$. Unlike previous algorithms for such norms, we give algorithms that are (1) deterministic, (2) work simultaneously for every $p \geq 1$, including $p = \infty$, and (3) can be implemented in both distributed and streaming environments. We study $\ell_p$-regression, entrywise $\ell_p$-low rank approximation, and versions of approximate matrix multiplication.
Author Information
Charlie Dickens (Alan Turing Institute & University of Warwick)
Graham Cormode (University of Warwick)
David Woodruff (Carnegie Mellon University)
Related Events (a corresponding poster, oral, or spotlight)
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2018 Oral: Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms »
Wed. Jul 11th 11:30 -- 11:50 AM Room K11
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