Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24820
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dc.contributor.authorMalik, Zeeshan-
dc.contributor.authorHussain, Amir-
dc.contributor.authorWu, Qingming Jonathan-
dc.date.accessioned2017-01-24T22:21:00Z-
dc.date.issued2016-11-14-
dc.identifier.urihttp://hdl.handle.net/1893/24820-
dc.description.abstractIn this paper, we consider the challenging problem of finding shared information in multiple data streams simultaneously. The standard statistical method for doing this is the well-known canonical correlation analysis (CCA) approach. We begin by developing an online version of the CCA and apply it to reservoirs of an echo state network in order to capture shared temporal information in two data streams. We further develop the proposed method by forcing it to ignore shared information that is created from static values using derivative information. We finally develop a novel multi-set CCA method which can identify shared information in more than two data streams simultaneously. The comparative effectiveness of the proposed methods is illustrated using artificial and real benchmark datasets.en_UK
dc.language.isoen-
dc.publisherSpringer-
dc.relationMalik Z, Hussain A & Wu QJ (2016) Extracting online information from dual and multiple data streams (Forthcoming/Available Online), Neural Computing and Applications.-
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Publisher policy allows this work to be made available in this repository; The original publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2647-3-
dc.subjectCanonical correlation analysisen_UK
dc.subjectEcho state networken_UK
dc.subjectGeneralized eigenvalue problemen_UK
dc.subjectHigh-variance feature-extractionen_UK
dc.subjectNeural networken_UK
dc.subjectUnsupervised learningen_UK
dc.titleExtracting online information from dual and multiple data streams (Forthcoming/Available Online)en_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2019-10-24T00:00:00Z-
dc.rights.embargoreasonUntil this work is formally published there will be an embargo on the full text of this work. Publisher requires embargo of 12 months after formal publication.-
dc.identifier.doihttp://dx.doi.org/10.1007/s00521-016-2647-3-
dc.citation.jtitleNeural Computing and Applications-
dc.citation.issn0941-0643-
dc.citation.publicationstatusIn press-
dc.citation.peerreviewedRefereed-
dc.type.statusPost-print (author final draft post-refereeing)-
dc.author.emailahu@cs.stir.ac.uk-
dc.citation.date14/11/2016-
dc.contributor.affiliationUniversity of Stirling-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.contributor.affiliationUniversity of Windsor-
dc.rights.embargoterms2019-10-24-
dc.rights.embargoliftdate2019-10-24-
Appears in Collections:Computing Science and Mathematics Journal Articles

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