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Non Euclidean metrics for similarity search in noisy datasets

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François, Damien [UCL] Wertz, Vincent [UCL] Verleysen, Michel [UCL]

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.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès restreint
Publication date 2005
Language Anglais
Conference "ESANN 2005, 13h European Symposium on Artificial Neural Networks", Bruges (Belgium) (du 27/04/2005 au 29/04/2005)
Peer reviewed yes
Host document "Proceedings of ESANN 2005, 13h European Symposium on Artificial Neural Networks"- p. 339-344
Affiliations UCL - FSA/ELEC - Département d'électricité
UCL - FSA/INMA - Département d'ingénierie mathématique
UCL - UCL/UCL - Secteur UCL
Keywords Euclidean distance ; Noisy datasets
Links
Bibliographic reference François, Damien ; Wertz, Vincent ; Verleysen, Michel. Non Euclidean metrics for similarity search in noisy datasets.ESANN 2005, 13h European Symposium on Artificial Neural Networks (Bruges (Belgium), du 27/04/2005 au 29/04/2005). In: Proceedings of ESANN 2005, 13h European Symposium on Artificial Neural Networks, 2005, p. 339-344
Permanent URL http://hdl.handle.net/2078.1/93832