Non Euclidean metrics for similarity search in noisy datasets
(2005) ESANN 2005, 13h European Symposium on Artificial Neural Networks — Location: Bruges (Belgium) (27.April.2005)
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- Abstract
- In the context of classification, the dissimilarity between data elements is often measured by a metric defined on the data space. Often, the choice of the metric is often disregarded and the Euclidean distance is used without further inquiries. This paper illustrates the fact that when other noise schemes than the white Gaussian noise are encountered, it can be interesting to use alternative metrics for similarity search.
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François, D., Wertz, V., & Verleysen, M. (2005). Non Euclidean metrics for similarity search in noisy datasets. Proceedings of ESANN 2005, 13h European Symposium on Artificial Neural Networks, p. 339-344. https://hdl.handle.net/2078.5/253831