A consistency theorem for randomized singular value decomposition
Ting-Li Chen,
Su-Yun Huang and
Weichung Wang
Statistics & Probability Letters, 2020, vol. 161, issue C
Abstract:
The singular value decomposition (SVD) and the principal component analysis are fundamental tools and probably the most popular methods for data dimension reduction. The rapid growth in the size of data matrices has lead to a need for developing efficient large-scale SVD algorithms. Randomized SVD was proposed, and its potential was demonstrated for computing a low-rank SVD (Rokhlin et al., 2009). In this article, we introduce a consistency notion for random projections used in randomized SVD and provide a consistency theorem for it. We also present a numerical example to show how the random projections to low dimension affect the consistency.
Keywords: Consistency; Randomized algorithm; Singular value decomposition; Principal component analysis (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167715220300468
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:161:y:2020:i:c:s0167715220300468
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
DOI: 10.1016/j.spl.2020.108743
Access Statistics for this article
Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul
More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().