Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/44918
Type: Conference paper
Title: A nonparametric bayesian method for inferring features from similarity judgments
Author: Navarro, D.
Griffiths, T.
Citation: Advances in Neural Information Processing Systems 19 (NIPS 2006), 2007 / Schölkopf, B., Platt, J.C., Hoffman, T. (ed./s), pp.1033-1040
Publisher: Morgan Kaufmann Publishers, Inc.
Issue Date: 2007
ISBN: 9780262195683
ISSN: 1049-5258
Conference Name: Neural Information Processing Systems 2006 (04 Dec 2006 - 07 Dec 2006 : Vancouver, Canada)
Statement of
Responsibility: 
Daniel J. Navarro, Thomas L. Griffiths
Abstract: 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.
Rights: © Authors
RMID: 0020076966
Published version: http://papers.nips.cc/paper/3136-a-nonparametric-bayesian-method-for-inferring-features-from-similarity-judgments
Appears in Collections:Psychology publications

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