Correlated Non-Parametric Latent Feature Models
Abstract
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated non-parametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
Cite
Text
Doshi-Velez and Ghahramani. "Correlated Non-Parametric Latent Feature Models." Conference on Uncertainty in Artificial Intelligence, 2009.Markdown
[Doshi-Velez and Ghahramani. "Correlated Non-Parametric Latent Feature Models." Conference on Uncertainty in Artificial Intelligence, 2009.](https://mlanthology.org/uai/2009/doshivelez2009uai-correlated/)BibTeX
@inproceedings{doshivelez2009uai-correlated,
title = {{Correlated Non-Parametric Latent Feature Models}},
author = {Doshi-Velez, Finale and Ghahramani, Zoubin},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2009},
pages = {143-150},
url = {https://mlanthology.org/uai/2009/doshivelez2009uai-correlated/}
}