Haussdorff and hellinger for colorimetric sensor array classification

Alstrøm, T. S., Sand Jensen, B., Schmidt, M. N., Kostesha, N. V. and Larsen, J. (2012) Haussdorff and hellinger for colorimetric sensor array classification. In: 2012 IEEE International Workshop on Machine Learning for Signal Processing, Santander, Spain, 23-26 Sep 2012, pp. 1-6. ISBN 9781467310246 (doi:10.1109/MLSP.2012.6349724)

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Abstract

Development of sensors and systems for detection of chemical compounds is an important challenge with applications in areas such as anti-terrorism, demining, and environmental monitoring. A newly developed colorimetric sensor array is able to detect explosives and volatile organic compounds; however, each sensor reading consists of hundreds of pixel values, and methods for combining these readings from multiple sensors must be developed to make a classification system. In this work we examine two distance based classification methods, K-Nearest Neighbor (KNN) and Gaussian process (GP) classification, which both rely on a suitable distance metric. We evaluate a range of different distance measures and propose a method for sensor fusion in the GP classifier. Our results indicate that the best choice of distance measure depends on the sensor and the chemical of interest.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jensen, Dr Bjorn Sand
Authors: Alstrøm, T. S., Sand Jensen, B., Schmidt, M. N., Kostesha, N. V., and Larsen, J.
College/School:College of Science and Engineering > School of Computing Science
ISSN:1551-2541
ISBN:9781467310246
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