Variational Inference for the Indian Buffet Process

Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:137-144, 2009.

Abstract

The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-doshi09a, title = {Variational Inference for the Indian Buffet Process}, author = {Finale Doshi and Kurt Miller and Jurgen Van Gael and Yee Whye Teh}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {137--144}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/doshi09a/doshi09a.pdf}, url = {http://proceedings.mlr.press/v5/doshi09a.html}, abstract = {The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets.} }
Endnote
%0 Conference Paper %T Variational Inference for the Indian Buffet Process %A Finale Doshi %A Kurt Miller %A Jurgen Van Gael %A Yee Whye Teh %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-doshi09a %I PMLR %J Proceedings of Machine Learning Research %P 137--144 %U http://proceedings.mlr.press %V 5 %W PMLR %X The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets.
RIS
TY - CPAPER TI - Variational Inference for the Indian Buffet Process AU - Finale Doshi AU - Kurt Miller AU - Jurgen Van Gael AU - Yee Whye Teh BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-doshi09a PB - PMLR SP - 137 DP - PMLR EP - 144 L1 - http://proceedings.mlr.press/v5/doshi09a/doshi09a.pdf UR - http://proceedings.mlr.press/v5/doshi09a.html AB - The Indian Buffet Process (IBP) is a nonparametric prior for latent feature models in which observations are influenced by a combination of several hidden features. For example, images may be composed of several objects or sounds may consist of several notes. Latent feature models seek to infer what these latent features from a set of observations. Current inference methods for the IBP have all relied on sampling. While these methods are guaranteed to be accurate in the limit, in practice, samplers for the IBP tend to mix slow. We develop a deterministic variational method for the IBP. We provide theoretical guarantees on its truncation bounds and demonstrate its superior performance for high dimensional data sets. ER -
APA
Doshi, F., Miller, K., Gael, J.V. & Teh, Y.W.. (2009). Variational Inference for the Indian Buffet Process. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:137-144

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