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Distance metric is widely used in similarity estimation. In this paper we find that the most popular Euclidean and Manhattan distance may not be suitable for all data distributions. A general guideline to establish the relation between a distribution model and its corresponding similarity estimation is proposed. Based on maximum likelihood theory, we propose new distance metrics, such as harmonic...
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