Please use this identifier to cite or link to this item:
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Extracting online information from dual and multiple data streams
Author(s): Malik, Zeeshan
Hussain, Amir
Wu, Qingming Jonathan
Contact Email:
Keywords: Canonical correlation analysis
Echo state network
Generalized eigenvalue problem
High-variance feature-extraction
Neural network
Unsupervised learning
Issue Date: 1-Jul-2018
Date Deposited: 24-Jan-2017
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.
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.
DOI Link: 10.1007/s00521-016-2647-3
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
Notes: Online publication date: 14 Nov 2016

Files in This Item:
File Description SizeFormat 
sfa.pdfFulltext - Accepted Version989.12 kBAdobe PDFView/Open

This item is protected by original copyright

Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved

If you believe that any material held in STORRE infringes copyright, please contact providing details and we will remove the Work from public display in STORRE and investigate your claim.