A nonparametric Bayesian method for inferring features from similarity judgments

Daniel J. Navarro, Thomas L. Griffiths

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages1033-1040
Number of pages8
StatePublished - Dec 1 2007
Externally publishedYes
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period12/4/0612/7/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Navarro, D. J., & Griffiths, T. L. (2007). A nonparametric Bayesian method for inferring features from similarity judgments. In Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 1033-1040). (Advances in Neural Information Processing Systems).