NumPy implementation of infinite latent feature model (aka Indian Buffet Process or IBP)
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README.md

PYTHON IBP (PyIBP)

David Andrzejewski (andrzeje@cs.wisc.edu) Department of Computer Sciences University of Wisconsin-Madison, USA

Example output plots

DESCRIPTION

This code uses NumPy and SciPy to efficiently implement "accelerated" Gibbs sampling [1] for the linear-Gaussian infinite latent feature model (aka Indian Buffet Process or IBP) [2].

This code also allows the use real-valued latent features [3], using a novel slice sampler for compatibility with the accelerated Gibbs scheme [5].

New features are sampled using a Metropolis-Hastings scheme [4].

EXAMPLE USAGE

See ./example/example.py for example usage on a simple synthetic dataset consisting of latent factors with nice structure. This dataset is derived from the one packaged with Finale Doshi-Velez's accelerated IBP code (http://people.csail.mit.edu/finale), but my understanding is that it originally appeared in earlier IBP work [2].

This example also can be easily run with make.

ACKNOWLEDGEMENTS

Thanks to Finale Doshi-Velez for making MATLAB code available and for answering detailed questions about inference. The modified slice sampler for real-valued latent features was the result of discussions with David Knowles.

Thanks to Christine Chai for contributing an easier-to-use Makefile for running the example.

LICENSE

This software is open-source, released under the terms of the GNU General Public License version 3, or any later version of the GPL (see COPYING).

REFERENCES

[1] Accelerated Gibbs sampling for the Indian buffet process Finale Doshi and Zoubin Ghahramani, ICML 2009

[2] Infinite latent feature models and the Indian buffet process Tom Griffiths and Zoubin Ghahramani, NIPS 2006

[3] Infinite Sparse Factor Analysis and Infinite Independent Components Analysis David Knowles and Zoubin Ghahramani, ICA 2007

[4] Modeling Dyadic Data with Binary Latent Factors Edward Meeds, Zoubin Ghahramani, Radford Neal, and Sam Roweis, NIPS 2006

[5] Accelerated Gibbs Sampling for Infinite Sparse Factor Analysis David Andrzejewski, LLNL Technical Report (LLNL-TR-499647)

[6] STA663 Statistical Computation Final Project - Implementation of the Indian Buffet Process (IBP). Christine P. Chai