@inproceedings{203bed3dec194771854dd293847e8465,

title = "An information theoretic analysis of maximum likelihood mixture estimation for exponential families",

abstract = "An important task in unsupervised learning is maximum likelihood mixture estimation (MLME) for exponential families. In this paper, we prove a mathematical equivalence between this MLME problem and the rate distortion problem for Bregman divergences. We also present new theoretical results in rate distortion theory for Bregman divergences. Further, an analysis of the problems as a trade-off between compression and preservation of information is presented that yields the information bottleneck method as an interesting special case.",

author = "Arindam Banerjee and Inderjit Dhillon and Joydeep Ghosh and Srujana Merugu",

year = "2004",

language = "English (US)",

isbn = "1581138385",

series = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004",

pages = "57--64",

editor = "R. Greiner and D. Schuurmans",

booktitle = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004",

note = "Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 ; Conference date: 04-07-2004 Through 08-07-2004",

}