A new algorithm for learning featural representations from similarity data is proposed. This algorithm infers models for a given number of features by numerically sampling the posterior distribution of a Bayesian model of similarity data, and applies a Bayesian model selection approach to choosing the appropriate number of features. The approach is demonstrated an experiment involving similarity judgments of circles, squares, and triangles colored red, green, and blue. |