Skip to Main content Skip to Navigation
Conference papers

Bayesian feature weighting for unsupervised learning, with application to object recognition

Abstract : We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.
Keywords : LEAR
Document type :
Conference papers
Complete list of metadata

https://hal.inria.fr/inria-00548235
Contributor : THOTH Team Connect in order to contact the contributor
Submitted on : Monday, December 20, 2010 - 2:51:34 PM
Last modification on : Friday, December 18, 2020 - 5:30:02 PM
Long-term archiving on: : Monday, March 21, 2011 - 2:46:41 AM

File

shrinkage.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00548235, version 1

Citation

Peter Carbonetto, Nando de Freitas, Paul Gustafson, Natalie Thompson. Bayesian feature weighting for unsupervised learning, with application to object recognition. Artificial Intelligence and Statistics (AI & Statistics '03), Microsoft, Jan 2003, Key West, United States. ⟨inria-00548235⟩

Share

Metrics

Record views

72

Files downloads

104