Bayesian nonparametric latent feature models

@inproceedings{Ghahramani2007BayesianNL,
  title={Bayesian nonparametric latent feature models},
  author={Zoubin Ghahramani and Thomas L. Griffiths},
  year={2007}
}
We describe a flexible nonparametric approach to latent variable modelling in which the number of latent variables is unbounded. This approach is based on a probability distribution over equivalence classes of binary matrices with a finite number of rows, corresponding to the data points, and an unbounded number of columns, corresponding to the latent variables. Each data point can be associated with a subset of the possible latent variables, which we refer to as the latent features of that… CONTINUE READING
Highly Influential
This paper has highly influenced 17 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 90 citations. REVIEW CITATIONS
65 Extracted Citations
31 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.

91 Citations

051015'08'10'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 91 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.

Similar Papers

Loading similar papers…