Bearing Similarity Measures for Self-Organizing Feature Maps

Keeratipranon, Narongdech & Maire, Frederic D. (2005) Bearing Similarity Measures for Self-Organizing Feature Maps. In Gallagher, Marcus, Hogan, James, & Maire, Frederic (Eds.) Intelligent Data Engineering and Automated Learning, 6 - 8 July 2005, Brisbane, Australia.

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The neural representation of space in rats has inspired many navigation systems for robots. In particular, Self-Organizing (Feature) Maps (SOM) are often used to give a sense of location to robots by mapping sensor information to a low-dimensional grid. For example, a robot equipped with a panoramic camera can build a 2D SOM from vectors of landmark bearings. If there are four landmarks in the robot's environment, then the 2D SOM is embedded in a 2D manifold lying in a 4D space. In general, the set of observable sensor vectors form a low-dimensional Riemannian manifold in a high-dimensional space. In a landmark bearing sensor space, the manifold can have a large curvature in some regions (when the robot is near a landmark for example), making the Eulidian distance a very poor approximation of the Riemannian metric. In this paper, we present and compare three methods for measuring the similarity between vectors of landmark bearings. We also discuss a method to equip SOM with a good approximation of the Riemannian metric. Although we illustrate the techniques with a landmark bearing problem, our approach is applicable to other types of data sets.

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2 citations in Scopus
1 citations in Web of Science®
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ID Code: 5070
Item Type: Conference Paper
Refereed: Yes
Keywords: SOM, Dissimilarity measurement, Mahalanobis distance
DOI: 10.1007/11508069_38
ISBN: 9783540269724
ISSN: 1611-3349
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Simulation and Modelling (080110)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2005 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. Lecture Notes in Computer Science
Deposited On: 06 Oct 2006 00:00
Last Modified: 29 Feb 2012 13:12

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