|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Extracting online information from dual and multiple data streams|
Wu, Qingming Jonathan
|Keywords:||Canonical correlation analysis|
Echo state network
Generalized eigenvalue problem
|Citation:||Malik Z, Hussain A & Wu QJ (2018) Extracting online information from dual and multiple data streams. Neural Computing and Applications, 30 (1), pp. 87-98. https://doi.org/10.1007/s00521-016-2647-3|
|Abstract:||In 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.|
|Rights:||This 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|
|Notes:||Online publication date: 14 Nov 2016|
|sfa.pdf||Fulltext - Accepted Version||989.12 kB||Adobe PDF||View/Open|
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