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.
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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|Item Type:||Journal Article|
|Keywords:||feature representation, autonomous vehicles, machine Learning Methods|
|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 15:27|
|Last Modified:||01 Mar 2012 10:35|
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