Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski p-Norms

[edit]

Graham Cormode, Charlie Dickens, David Woodruff ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1048-1056, 2018.

Abstract

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 simultaneouslyfor 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.

Related Material