Probabilistic Multidimensional Scaling Using a City-Block Metric.
Using a probabilistic model, exact and approximate probability density functions (PDFs) for city-block distances and distance ratios are developed. The model assumes that stimuli can be represented by random vectors having multivariate normal distributions. Comparisons with the more common Euclidean PDFs are presented. The potential ability of the proposed model to correctly detect Euclidean and city-block metrics is briefly investigated. These results are then contrasted to those obtained using a deterministic, nonmetric model. Copyright 2001 Academic Press.
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