Sequential nonlinear manifold learning

Article type: Research Article

Authors: Kumar, S. | Guivant, J. | Upcroft, B. | Durrant-Whyte, H.F.

Affiliations: Australian Centre for Field Robotics, University of Sydney, NSW 2006, Australia. Tel.: +612 93512186; Fax: +612 93517474; E-mail: [email protected]

Abstract: 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.

Keywords: Feature representation, autonomous vehicles, machine learning

DOI: 10.3233/IDA-2007-11207

Journal: Intelligent Data Analysis, vol. 11, no. 2, pp. 203-222, 2007