Extracting online information from dual and multiple data streams

  • Zeeshan Khawar Malik
  • Amir Hussain
  • Q. M. Jonathan Wu
Original Article


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.


Canonical correlation analysis Echo state network Generalized eigenvalue problem High-variance feature-extraction Neural network Unsupervised learning 



The first author is grateful to Professor Colin Fyfe, formerly with the University of The West of Scotland, for his insightful suggestions which helped improve the writing of this paper. This work of Zeeshan Malik was supported by University of The Punjab, Lahore, Pakistan, as part of the Overseas Scholarship for his Doctoral Studies in the University of Stirling, UK.


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Zeeshan Khawar Malik
    • 1
  • Amir Hussain
    • 1
  • Q. M. Jonathan Wu
    • 2
  1. 1.University of StirlingStirlingScotland, UK
  2. 2.University of WindsorWindsor Canada

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