@CONFERENCE\{IMM2012-06418,
    author       = "T. S. Alstr{\o}m and B. S. Jensen and M. N. Schmidt and N. V. Kostesha and J. Larsen",
    title        = "Haussdorff and Hellinger for Colorimetric Sensor Array Classification",
    year         = "2012",
    month        = "sep",
    booktitle    = "{IEEE} International Workshop on Machine Learning for Signal Processing",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6418-full.html",
    abstract     = "Development of sensors and systems for detection of chem ical 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."
}