Jaakko Peltonen, Janne Sinkkonen and Samuel Kaski. Sequential Information Bottleneck for Finite Data. In: Proceedings of the 21st International Conference on Machine Learning, Banff, Canada, 2004. Accepted for publication. (pdf)

The sequential information bottleneck (sIB) algorithm clusters co-occurrence data such as text documents vs. words. We introduce a variant that models sparse co-occurrence data by a generative process. This turns the objective function of sIB, mutual information, into a Bayes factor, while keeping it intact asymptotically, for non-sparse data. Experimental performance of the new algorithm is comparable to the original sIB for large data sets, and better for smaller, sparse sets.