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Identification Rates for Block-correlated Gaussian Sources
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 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS / [ed] Matthews, M B, IEEE , 2018, s. 2114-2118Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Among many current data processing systems, the objectives are often not the reproduction of data, but to compute some answers based on the data responding to sonic queries. The similarity identification task is to identify the items in a database which are similar to a given query item regarding to a certain metric. The problem of compression for similarity identification has been studied in [1]. Unlike classic compression problems, the focus is not on reconstructing the original data. Instead, the compression rate is determined by the desired reliability of the answers. Specifically, the information measure identification rate of a compression scheme characterizes the minimum compression rate that can be achieved which guarantees reliable answers with respect to a given similarity threshold. In this paper, we study the component-based quadratic similarity identification for correlated sources. The blocks are first decorrelated by Karhunen-Loeve transform. Then, the decorrelated data is processed by a distinct D-admissible system for each component. We derive the identification rate of component-based scheme for block correlated Gaussian sources. In addition, we characterize the identification rate of a special setting where any information regarding to the component similarity thresholds is unknown while only the similarity threshold of the whole scheme is given. Furthermore, we prove that block-correlated Gaussian sources are the "most difficult" to code under the special setting.

sted, utgiver, år, opplag, sider
IEEE , 2018. s. 2114-2118
Serie
Conference Record of the Asilomar Conference on Signals Systems and Computers, ISSN 1058-6393
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-252677DOI: 10.1109/ACSSC.2018.8645306ISI: 000467845100373Scopus ID: 2-s2.0-85062960125ISBN: 978-1-5386-9218-9 (tryckt)OAI: oai:DiVA.org:kth-252677DiVA, id: diva2:1319747
Konferanse
52nd Asilomar Conference on Signals, Systems, and Computers, OCT 28-NOV 01, 2018, Pacific Grove, CA
Merknad

QC 20190603

Tilgjengelig fra: 2019-06-03 Laget: 2019-06-03 Sist oppdatert: 2019-07-31bibliografisk kontrollert

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