Information Bottleneck for Gaussian Variables

Chechik, Gal, Globerson, Amir, Tishby, Naftali, Weiss, Yair

Neural Information Processing Systems 

The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another - relevance - variable. An interesting question addressed in the current work is the extension of these ideas to obtain continuous representations that preserve relevant information, rather than discrete clusters. We give a formal definition of the general continuous IB problem and obtain an analytic solution for the optimal representation for the important case of multivariate Gaussian variables.

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