TY - GEN
T1 - A nonparametric Bayesian method for inferring features from similarity judgments
AU - Navarro, Daniel J.
AU - Griffiths, Thomas L.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=55749097388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55749097388&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:55749097388
SN - 9780262195683
T3 - Advances in Neural Information Processing Systems
SP - 1033
EP - 1040
BT - Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
T2 - 20th Annual Conference on Neural Information Processing Systems, NIPS 2006
Y2 - 4 December 2006 through 7 December 2006
ER -