Haussdorff and hellinger for colorimetric sensor array classification

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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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.
Original languageEnglish
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages6
Place of publication978-1-4673-1025-3
PublisherIEEE
Publication date2012
ISBN (print)978-1-4673-1024-6
DOIs
StatePublished

Conference

Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
CountrySpain
CitySantander
Period23/10/1226/10/12
Internet addresshttp://mlsp2012.conwiz.dk/
NameMachine Learning for Signal Processing
ISSN (Print)1551-2541
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Hausdorff distance, Hellinger distance, Chemo–selective compounds, Feature extraction, K–nearest neighbor classification, Gaussian Process Classification
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