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dc.contributor.authorBennett, Charles H.en_US
dc.contributor.authorGacs, Peteren_US
dc.contributor.authorLi, Mingen_US
dc.contributor.authorVitanyi, Paul M.B.en_US
dc.contributor.authorZurek, Wojciech H.en_US
dc.date.accessioned2018-06-15T13:53:59Z
dc.date.available2018-06-15T13:53:59Z
dc.date.issued1998-07-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000074287700005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationCH Bennett, P Gacs, M Li, FMB Vitanyi, WH Zurek. 1998. "Information distance." IEEE Transactions On Information Theory, Volume 44, Issue 4, pp. 1407 - 1423 (17).
dc.identifier.issn0018-9448
dc.identifier.urihttps://hdl.handle.net/2144/29393
dc.description.abstractWhile Kolmogorov (1965) complexity is the accepted absolute measure of information content in an individual finite object, a similarly absolute notion is needed for the information distance between two individual objects, for example, two pictures. We give several natural definitions of a universal information metric, based on length of shortest programs for either ordinary computations or reversible (dissipationless) computations. It turns out that these definitions are equivalent up to an additive logarithmic term. We show that the information distance is a universal cognitive similarity distance. We investigate the maximal correlation of the shortest programs involved, the maximal uncorrelation of programs (a generalization of the Slepian-Wolf theorem of classical information theory), and the density properties of the discrete metric spaces induced by the information distances. A related distance measures the amount of nonreversibility of a computation. Using the physical theory of reversible computation, we give an appropriate (universal, antisymmetric, and transitive) measure of the thermodynamic work required to transform one object in another object by the most efficient process. Information distance between individual objects is needed in pattern recognition where one wants to express effective notions of "pattern similarity" or "cognitive similarity" between individual objects and in thermodynamics of computation where one wants to analyze the energy dissipation of a computation from a particular input to a particular output.en_US
dc.format.extentp. 1407 - 1423en_US
dc.languageEnglish
dc.publisherIEEEen_US
dc.relation.ispartofIEEE Transactions On Information Theory
dc.subjectScience & technologyen_US
dc.subjectTechnologyen_US
dc.subjectComputer science, information systemsen_US
dc.subjectEngineering, electrical & electronicen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.subjectAlgorithmic information theoryen_US
dc.subjectDescription complexityen_US
dc.subjectEntropyen_US
dc.subjectHeat dissipationen_US
dc.subjectInformation distanceen_US
dc.subjectInformation metricen_US
dc.subjectIrreversible computationen_US
dc.subjectKolmogorov complexityen_US
dc.subjectPattern recognitionen_US
dc.subjectReversible computationen_US
dc.subjectThermodynamics of computationen_US
dc.subjectUniversal cognitive distanceen_US
dc.subjectArtificial intelligence and image processingen_US
dc.subjectElectrical and electronic engineeringen_US
dc.subjectCommunications technologiesen_US
dc.subjectNetworking & telecommunicationsen_US
dc.titleInformation distanceen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/18.681318
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US


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