A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments

Part of Advances in Neural Information Processing Systems 19 (NIPS 2006)

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Daniel Navarro, Thomas Griffiths


The additive clustering model is widely used to infer the features of a set of stimuli from their similarities, on the assumption that similarity is a weighted linear function of common features. This paper develops a fully Bayesian formulation of the additive clustering model, using methods from nonparametric Bayesian statistics to allow the number of features to vary. We use this to explore several approaches to parameter estimation, showing that the nonparametric Bayesian approach provides a straightforward way to obtain estimates of both the number of features used in producing similarity judgments and their importance.