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Proceedings Paper

Median learning vector quantizer
Author(s): Ioannis Pitas; P. Kiniklis
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Paper Abstract

In this paper we propose a novel class of learning vector quantizers (LVQ) based on multivariate data ordering. Linear LVQ is not the optimal estimator for non-Gaussian multivariate data distributions. Furthermore, it is not robust either in the case of outliers or in the case of erroneous decisions. The novel LVQs use multivariate ordering in order to obtain location estimators that are robust and that provide superior and, in certain cases, optimal performance for non-Gaussian multivariate distributions. A special case of the novel LVQ class is the marginal median LVQ (MM LVQ), which uses the marginal median as multivariate estimator of location.

Paper Details

Date Published: 1 May 1994
PDF: 12 pages
Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); doi: 10.1117/12.172566
Show Author Affiliations
Ioannis Pitas, Univ. of Thessaloniki (Greece)
P. Kiniklis, Univ. of Thessaloniki (Greece)


Published in SPIE Proceedings Vol. 2180:
Nonlinear Image Processing V
Edward R. Dougherty; Jaakko Astola; Harold G. Longbotham, Editor(s)

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