Haussdorff and hellinger for colorimetric sensor array classification

Tommy Sonne Alstrøm, Bjørn Sand Jensen, Mikkel Nørgaard Schmidt, Natalie Kostesha, Jan Larsen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    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.
    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
    Publication statusPublished - 2012
    Event2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain
    Duration: 23 Oct 201226 Oct 2012
    http://mlsp2012.conwiz.dk/

    Conference

    Conference2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
    CountrySpain
    CitySantander
    Period23/10/201226/10/2012
    Internet address
    SeriesMachine Learning for Signal Processing
    ISSN1551-2541

    Keywords

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

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