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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presents an unsupervised dimensionality reduction algorithm which is based on the information bottleneck (IB) method. The method optimizes a constrained objective which, like the IB method, is comprised of the mutual information criteria. The criteria are between a joint density of discrete observed variables and the densities of a set of discrete latent factors, and between factored densities of the observed variables and the latent factors' densities. The goal of the the objective function is to infer latent factors such that by conditioning on them, the observed variables can be factorized into subsets of minimally correlated elements.