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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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
Glasgow Author(s) Enlighten ID:Jensen, Dr Bjorn
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
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