Infinite Latent Feature Models and the Indian Buffet Process
Abstract
We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features. We identify a simple generative process that results in the same distribution over equivalence classes, which we call the Indian buffet process. We illustrate the use of this distribution as a prior in an infinite latent feature model, deriving a Markov chain Monte Carlo algorithm for inference in this model and applying the algorithm to an image dataset.
Cite
Text
Ghahramani and Griffiths. "Infinite Latent Feature Models and the Indian Buffet Process." Neural Information Processing Systems, 2005.Markdown
[Ghahramani and Griffiths. "Infinite Latent Feature Models and the Indian Buffet Process." Neural Information Processing Systems, 2005.](https://mlanthology.org/neurips/2005/ghahramani2005neurips-infinite/)BibTeX
@inproceedings{ghahramani2005neurips-infinite,
title = {{Infinite Latent Feature Models and the Indian Buffet Process}},
author = {Ghahramani, Zoubin and Griffiths, Thomas L.},
booktitle = {Neural Information Processing Systems},
year = {2005},
pages = {475-482},
url = {https://mlanthology.org/neurips/2005/ghahramani2005neurips-infinite/}
}