Correlated Non-Parametric Latent Feature Models

Finale Doshi-Velez, Zoubin Ghahramani
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:143-150, 2009.

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 nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-doshi-velez09a, title = {Correlated Non-Parametric Latent Feature Models}, author = {Doshi-Velez, Finale and Ghahramani, Zoubin}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {143--150}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/doshi-velez09a/doshi-velez09a.pdf}, url = {https://proceedings.mlr.press/r7/doshi-velez09a.html}, 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 nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Correlated Non-Parametric Latent Feature Models %A Finale Doshi-Velez %A Zoubin Ghahramani %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-doshi-velez09a %I PMLR %P 143--150 %U https://proceedings.mlr.press/r7/doshi-velez09a.html %V R7 %X 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 nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets. %Z Reissued by PMLR on 04 October 2026.
APA
Doshi-Velez, F. & Ghahramani, Z.. (2009). Correlated Non-Parametric Latent Feature Models. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:143-150 Available from https://proceedings.mlr.press/r7/doshi-velez09a.html. Reissued by PMLR on 04 October 2026.

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