Low-Rank Approximation and Regression in Input Sparsity Time
Work
Year: 2017
Type: article
Abstract: We design a new distribution over m × n matrices S so that, for any fixed n × d matrix A of rank r , with probability at least 9/10, ∥ SAx ∥ 2 = (1 ± ε)∥ Ax ∥ 2 simultaneously for all x ∈ R d . Here, ... more
Source: Journal of the ACM
Institution IBM Research - Almaden
Cites: 73
Cited by: 229
Related to: 10
FWCI: 21.49
Citation percentile (by year/subfield): 91.19
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green