Data sketching for tracking large-scale dynamical processes

Dimitrios Berberidis, Georgios B. Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In a time when data increase massively in their volume, variety, and velocity, performing inference tasks by utilizing the available information in its entirety is not always an affordable option. The present paper proposes a data-driven measurement selection scheme to render tracking of large-scale dynamic processes affordable, by processing a reduced number of data. The proposed method processes observations sequentially, and extracts a low-complexity sketch that can be implemented in real-time. Furthermore, a low-complexity smoothing is developed as a means of mitigating the error performance degradation caused by dimensionality reduction. Simulations on synthetic data, compare the proposed methods with competing alternatives, and corroborate their efficacy in terms of estimation accuracy versus complexity reduction.

Original languageEnglish (US)
Title of host publicationConference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages345-349
Number of pages5
ISBN (Electronic)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2016-February
ISSN (Print)1058-6393

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/TerritoryUnited States
CityPacific Grove
Period11/8/1511/11/15

Bibliographical note

Funding Information:
Work in this paper was supported by NSF Grants No. 1514056 and 1500713, and NIH Grant No. 1R01GM104975-01

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