Paper Info
Title
Fast estimation of approximate matrix ranks using spectral densities.
Abstract
Many machine learning and data-related applications require the knowledge of approximate ranks of large data matrices at hand. This letter presents two computationally inexpensive techniques to estimate the approximate ranks of such matrices. These techniques exploit approximate spectral densities, popular in physics, which are probability density distributions that measure the likelihood of findi...
Year
DOI
Venue
2017
10.1162/NECO_a_00951
Neural Computation
DocType
Volume
Issue
Journal
29
5
ISSN
Citations 
PageRank 
0899-7667
7
0.49
References 
Authors
24
3
Name
Order
Citations
PageRank
shashanka ubaru1588.97
Yousef Saad21940254.74
Abd-Krim Seghouane319324.99