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Abstract: Assignment methods are at the heart of many algorithms for unsupervised learning and clustering --- in particular, the well-known K-means and Expectation-Maximization (EM) algorithms. In this work, we study several different methods of assignment, including the "hard" assignments used by K-means and the "soft" assignments used by EM. While it is known that K-means minimizes the distortion on the data and EM maximizes the likelihood, little is known about the systematic differences of behavior ... (Update)
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BibTeX entry: (Update)
Kearns, M., Mansour, Y., and Ng, A. Y. (1997). An information-theoretic analysis of hard and soft assignment methods for clustering. In Proceedings of Uncertainty in Artificial Intelligence. AAAI. http://citeseer.ist.psu.edu/kearns97informationtheoretic.html More
@inproceedings{ kearnsinformationtheoretic,
author = "Michael Kearns and Yishay Mansour and Andrew Y. Ng",
title = "An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering",
pages = "282--293",
url = "citeseer.ist.psu.edu/kearns97informationtheoretic.html" }
Citations (may not include all citations):2528 Maximum-likelihood from incomplete data via the em algorithm (context) - Dempster, Laird et al. - 1977
2319 Elements of Information Theory (context) - Cover, Thomas - 1991 ACM
2133 Pattern Classification and Scene Analysis (context) - Duda, Hart - 1973
1056 Introduction to the Theory of Neural Computation (context) - Hertz, Krogh et al. - 1991 ACM
653 Fundamentals of Speech Recognition (context) - Rabiner, Juang - 1993 ACM
283 Some methods for classification and analysis of multivariate.. - MacQueen - 1967
107 The EM algorithm for graphical association models with missi.. (context) - Lauritzen - 1995 ACM
93 IEEE Transactions on Information Theory (context) - Gersho, structure et al. - 1982
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