Haussdorff and hellinger for colorimetric sensor array classification

Research output: 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
Publication date2012
ISBN (Print)978-1-4673-1024-6
StatePublished - 2012
Event2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain
Duration: 23 Oct 201226 Oct 2012


Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Internet address
SeriesMachine Learning for Signal Processing
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Hausdorff distance, Hellinger distance, Chemo–selective compounds, Feature extraction, K–nearest neighbor classification, Gaussian Process Classification
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ID: 41012662