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PREDICTION-BASED SIMILARITY IDENTIFICATION FOR AUTOREGRESSIVE PROCESSES
KTH, Skolan för elektroteknik och datavetenskap (EECS), Teknisk informationsvetenskap.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Teknisk informationsvetenskap.ORCID-id: 0000-0001-9471-1409
KTH, Skolan för elektroteknik och datavetenskap (EECS), Teknisk informationsvetenskap.ORCID-id: 0000-0002-7807-5681
2018 (engelsk)Inngår i: 2018 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2018), IEEE , 2018, s. 266-270Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE , 2018. s. 266-270
Serie
IEEE Global Conference on Signal and Information Processing, ISSN 2376-4066
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-249832DOI: 10.1109/GlobalSIP.2018.8646407ISI: 000462968100054Scopus ID: 2-s2.0-85063103300ISBN: 978-1-7281-1295-4 (tryckt)OAI: oai:DiVA.org:kth-249832DiVA, id: diva2:1306088
Konferanse
IEEE Global Conference on Signal and Information Processing (GlobalSIP), NOV 26-29, 2018, Anaheim, CA
Merknad

QC 20190423

Tilgjengelig fra: 2019-04-23 Laget: 2019-04-23 Sist oppdatert: 2019-04-23bibliografisk kontrollert

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