Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/24820
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Extracting online information from dual and multiple data streams (Forthcoming/Available Online)
Authors: Malik, Zeeshan
Hussain, Amir
Wu, Qingming Jonathan
Contact Email: ahu@cs.stir.ac.uk
Keywords: Canonical correlation analysis
Echo state network
Generalized eigenvalue problem
High-variance feature-extraction
Neural network
Unsupervised learning
Issue Date: 14-Nov-2016
Citation: Malik Z, Hussain A & Wu QJ (2016) Extracting online information from dual and multiple data streams (Forthcoming/Available Online), Neural Computing and Applications.
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: http://dx.doi.org/10.1007/s00521-016-2647-3
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