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

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