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

David B. Mckay, Bryan Lilly

• 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.