Learning in high dimensions: Modular Mixture Models
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:8-12, 2001.
We present a new approach to learning prob- abilistic 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 pro- duces a total density estimate. We derive and demonstrate an iterative learning algorithm that uses only local information.