Abstract
Many learning algorithms rely on distance metrics to receive their input data. Research has shown that these metrics can improve the performance of these algorithms. Over the years an often popular function is the Euclidean function. In this paper, we investigate a number of different metrics proposed by different communities, including Mahalanobis, Euclidean, Kullback-Leibler and Hamming distance. Overall, the best-performing method is the Mahalanobis distance metric.
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Walters-Williams, J., Li, Y. (2010). Comparative Study of Distance Functions for Nearest Neighbors. In: Elleithy, K. (eds) Advanced Techniques in Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3660-5_14
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DOI: https://doi.org/10.1007/978-90-481-3660-5_14
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