Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition

Paul Gustafson, Peter Carbonetto, Natalie Thompson, Nando Freitas

AISTATS 2003 pp. 124-131

/aistats/2003/gustafson2003aistats-bayesian/

Abstract

We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model is a finite mixture of multivariate Gaussian distributions. We demonstrate how the model parameters and cluster assignments can be computed simultaneously 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 yield good results.

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Text

Gustafson et al. "Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.

Markdown

[Gustafson et al. "Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.](https://mlanthology.org/aistats/2003/gustafson2003aistats-bayesian/)

BibTeX

@inproceedings{gustafson2003aistats-bayesian,
  title     = {{Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition}},
  author    = {Gustafson, Paul and Carbonetto, Peter and Thompson, Natalie and Freitas, Nando},
  booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics},
  year      = {2003},
  pages     = {124-131},
  volume    = {R4},
  url       = {https://mlanthology.org/aistats/2003/gustafson2003aistats-bayesian/}
}