Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0001-9471-1409
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-7807-5681
2018 (English)In: 2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018), IEEE , 2018, p. 266-270Conference paper, Published paper (Refereed)
Abstract [en]

The task of similarity identification is to identify items in a database which are similar to a given query item for a given metric. The identification rate of a compression scheme characterizes the minimum rate that can be achieved which guarantees reliable answers with respect to a given similarity threshold [1]. In this paper, we study a prediction-based quadratic similarity identification for autoregressive processes. We use an ideal linear predictor to remove linear dependencies in autoregressive processes. The similarity identification is conducted on the residuals. We show that the relation between the distortion of query and database processes and the distortion of their residuals is characterized by a sequence of eigenvalues. We derive the identification rate of our prediction-based approach for autoregressive Gaussian processes. We characterize the identification rate for the special case where only the smallest value in the sequence of eigenvalues is required to be known and derive its analytical upper bound by approximating a sequence of matrices with a sequence of Toeplitz matrices.

Place, publisher, year, edition, pages
IEEE , 2018. p. 266-270
Series
IEEE Global Conference on Signal and Information Processing, ISSN 2376-4066
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-249832DOI: 10.1109/GlobalSIP.2018.8646407ISI: 000462968100054Scopus ID: 2-s2.0-85063103300ISBN: 978-1-7281-1295-4 (print)OAI: oai:DiVA.org:kth-249832DiVA, id: diva2:1306088
Conference
IEEE Global Conference on Signal and Information Processing (GlobalSIP), NOV 26-29, 2018, Anaheim, CA
Note

QC 20190423

Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-04-23Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Wu, HanweiWang, QiwenFlierl, Markus

Search in DiVA

By author/editor
Wu, HanweiWang, QiwenFlierl, Markus
By organisation
Information Science and Engineering
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 3 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf