Control variates as a variance reduction technique for random projections

Keegan Kang*, Giles Hooker

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Control variates are used as a variance reduction technique in Monte Carlo integration, making use of positively correlated variables to bring about a reduction of variance for estimated data. By storing the marginal norms of our data, we can use control variates to reduce the variance of random projection estimates. We demonstrate the use of control variates in estimating the Euclidean distance and inner product between pairs of vectors, and give some insight on our control variate correction. Finally, we demonstrate our variance reduction through experiments on synthetic data and the arcene, colon, kos, nips datasets. We hope that our work provides a starting point for other control variate techniques in further random projection applications.

Original languageEnglish
Title of host publicationPattern Recognition Applications and Methods - 6th International Conference, ICPRAM 2017, Revised Selected Papers
EditorsAna Fred, Maria De Marsico, Gabriella Sanniti di Baja
PublisherSpringer Verlag
Pages1-20
Number of pages20
ISBN (Print)9783319936468
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017 - Porto, Portugal
Duration: 24 Feb 201726 Feb 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10857 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2017
Country/TerritoryPortugal
CityPorto
Period24/02/1726/02/17

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