  Learning with hidden variables is a central challenge in probabilistic
graphical models that has important implications for many real-life problems.
The classical approach is using the Expectation Maximization (EM) algorithm.
This algorithm, however, can get trapped in local maxima. In this paper we
explore a new approach that is based on the Information Bottleneck principle.
In this approach, we view the learning problem as a tradeoff between two
information theoretic objectives. The first is to make the hidden variables
uninformative about the identity of specific instances. The second is to make
the hidden variables informative about the observed attributes. By exploring
different tradeoffs between these two objectives, we can gradually converge on
a high-scoring solution. As we show, the resulting, Information Bottleneck
Expectation Maximization (IB-EM) algorithm, manages to find solutions that are
superior to standard EM methods.
