Learning in High Dimensions: Modular Mixture Models
Abstract
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.
Cite
Text
Attias. "Learning in High Dimensions: Modular Mixture Models." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.Markdown
[Attias. "Learning in High Dimensions: Modular Mixture Models." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/attias2001aistats-learning/)BibTeX
@inproceedings{attias2001aistats-learning,
title = {{Learning in High Dimensions: Modular Mixture Models}},
author = {Attias, Hagai},
booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
year = {2001},
pages = {8-12},
volume = {R3},
url = {https://mlanthology.org/aistats/2001/attias2001aistats-learning/}
}