# Cascaded Redundancy Reduction

Virginia R. de Sa and Geoffrey E. Hinton

Department of Computer Science

University of Toronto

**Abstract**

We describe a method of incrementally constructing a hierarchical
generative model of an ensemble of binary data vectors. The model is composed of
stochastic, binary, logistic units. Hidden units are added to the model one at a
time with the goal of minimizing the information required to describe the data vectors
using the model. In addition to the top-down generative weights that define the
model, there are bottom-up recognition weights that determine the binary states of the
hidden units given a data vector. Even though the stochastic generative model can
produce each data vector in many ways, the recognition model is forced to pick just one of
these ways. The recognition model therefore underestimates the ability of the
generative model to predict the data, but this underestimation greatly simplifies the
process of searching for the generative and recognition weights of a new hidden unit.

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*In Network: Computation in Neural Systems 9 (1) 1998*

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