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

1 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 publicationNIPS 2006
Subtitle of host publicationProceedings of the 19th International Conference on Neural Information Processing Systems
EditorsBernhard Scholkopf, John C. Platt, Thomas Hofmann
PublisherMIT Press Journals
Pages1033-1040
Number of pages8
ISBN (Electronic)0262195682, 9780262195683
StatePublished - 2006
Externally publishedYes
Event19th International Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameNIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Conference

Conference19th International Conference on Neural Information Processing Systems, NIPS 2006
Country/TerritoryCanada
CityVancouver
Period12/4/0612/7/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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