Sequential nonlinear manifold learning

Kumar, S., Guivant, J., Upcroft, B., & Durrant-Whyte, H. F. (2007) Sequential nonlinear manifold learning. Intelligent Data Analysis, 11(2), pp. 203-222.


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The computation of compact and meaningful representations of high dimensional sensor data has recently been addressed through the development of Nonlinear Dimensional Reduction (NLDR) algorithms. The numerical implementation of spectral NLDR techniques typically leads to a symmetric eigenvalue problem that is solved by traditional batch eigensolution algorithms. The application of such algorithms in real-time systems necessitates the development of sequential algorithms that perform feature extraction online. This paper presents an efficient online NLDR scheme, Sequential-Isomap, based on incremental singular value decomposition (SVD) and the Isomap method. Example simulations demonstrate the validity and significant potential of this technique in real-time applications such as autonomous systems.

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1 citations in Scopus
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1 citations in Web of Science®

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100 since deposited on 10 May 2011
1 in the past twelve months

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ID Code: 40421
Item Type: Journal Article
Refereed: Yes
Keywords: feature representation, autonomous vehicles, machine Learning Methods
ISSN: 1088467X
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2007 IOS Press
Deposited On: 10 May 2011 05:27
Last Modified: 01 Mar 2012 00:35

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