This paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An al- gorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing the appropriate number of dimensions and features is developed. The approach is demonstrated on a classic data set that considers the similarities between the numbers 0 through 9.

The dimensional and featural approaches have different strengths and weaknesses. Dimensional representations are constrained by the metric axioms, such as the tri-- angle inequality, that are violated by some empirical data.