Learning in high dimensions: modular mixture models

We present a new approach to learning probabilistic models for high dimensional data. This approach divides the data dimensions into low dimensional subspaces, and learns a separate mixture model for each subspace. The models combine in a principled manner to form a flexible modular network that produces a total density estimate. We derive and demonstrate an iterative learning algorithm that uses only local information.