Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences


UC Berkeley


2008 Research Summary

Classification of Images with Hierarchical Beta Processes

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Romain Jean Thibaux, Michael Jordan and Erik Sudderth

The Indian buffet process [1] is a prior for binary vector observations. It is a nonparametric prior, allowing binary vectors of unbounded length. When representing data such as text as binary vectors--for instance each bit may represent the presence of a word--this prior gives Bayesian estimates of the probability of each bit. Used on texts of various categories or topics, a Naive Bayes classifier can be constructed based on these estimates.

A critical component of Naive Bayes is the smoothing and shrinking of parameters, through the sharing of statistical strengh between categories. Hierarchical beta processes [2] are an extension of Indian buffet processes that allow such sharing and outperform simpler smoothing methods. We now extend this work to categories of images, where obtaining a binary representation is less straightforward.

T. Griffiths and Z. Ghahramani, "Infinite Latent Feature Models and the Indian Buffet Process," Advances in Neural Information Processing Systems (NIPS), Vol. 18, 2005.
R. Thibaux and M. I. Jordan, "Hierarchical Beta Processes and the Indian Buffet Process," Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007), San Juan, Puerto Rico, 2007.