ICML 2015poster3 citations
Information Geometry and Minimum Description Length Networks
Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet
Abstract
We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based on minimum description length, we derive a simple geometric principle to learn all these models together. We present a new learning machine with theories, algorithms, and simulations.
BibTeX
@InProceedings{pmlr-v37-suna15,
title = {Information Geometry and Minimum Description Length Networks},
author = {Sun, Ke and Wang, Jun and Kalousis, Alexandros and Marchand-Maillet, Stephan},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {49--58},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/suna15.pdf},
url = {https://proceedings.mlr.press/v37/suna15.html},
abstract = {We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based on minimum description length, we derive a simple geometric principle to learn all these models together. We present a new learning machine with theories, algorithms, and simulations.}
}