Abstract
The Mahalanobis distance (MD) is a widely used measure in Statistics and Pattern Recognition. Interestingly, assuming that the data are generated from a Gaussian distribution, it considers the covariance matrix to evaluate the distance between a data point and the distribution mean. In this work, we generalize MD for distributions in the exponential family, providing both, a definition in terms of the data density function and a computable version. We show its performance on several artificial and real data scenarios.
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Martos, G., Muñoz, A., González, J. (2013). On the Generalization of the Mahalanobis Distance. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2013. Lecture Notes in Computer Science, vol 8258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41822-8_16
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DOI: https://doi.org/10.1007/978-3-642-41822-8_16
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