Correlated Non-Parametric Latent Feature Models
Finale Doshi-Velez, Zoubin Ghahramani
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.
PDF Link: /papers/09/p143-doshi-velez.pdf
AUTHOR = "Finale Doshi-Velez
and Zoubin Ghahramani",
TITLE = "Correlated Non-Parametric Latent Feature Models",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "143--150"