Percept Variance, Subadditivity and the Metric Classification of Similarity, and Dissimilarity Data

• Autores: David B. Mckay, Bryan Lilly
• Localización: Journal of classification, ISSN 0176-4268, Vol. 21, Nº 2, 2004, págs. 185-206
• Idioma: inglés
• DOI: 10.1007/s00357-004-0016-x
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• Resumen

  • Percept variance is shown to change the additive property of city-block distances and make city-block distances more subadditive than Euclidean distances. Failure to account for percept variance will result in the misclassification of city-block data as Euclidean. A maximum likelihood estimation procedure is proposed for the multidimensional scaling of similarity data characterized by percept variance. Monte Carlo and empirical experiments are used to evaluate the proposed approach.