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Abstract: In this paper we describe an approach to model selection in unsupervised learning. This approach determines both the feature set and the number of clusters. To this end we first derive an objective function that explicitly incorporates this generalization. We then evaluate two schemes for model selection - one using this objective function (a Bayesian estimation scheme that selects the best model structure using the marginal or integrated likelihood) and the second based on a technique using a... (Update)
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...and evaluation. Finally Section 5 describes some future work in this area. 2 Models for Unsupervised Learning In previous work[14, 13] we addressed the problem of flat (non hierarchical) partitional (each data element belongs to one and only one cluster) clustering. Here...
.... of the diversity of two distributions known as mutual information (MI) This criterion that we will refer to as MIC has been suggested in [21] for unsupervised clustering and we found it rather informative. We have not chosen another criterion such as predicting occurrences of...
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2: Elements of Information Theory (context) - Cover, Thomas - 1991
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BibTeX entry: (Update)
Shivakumar Vaithyanathan and Byron Dom. Generalized model selection for unsupervised learning in high dimensions. In S. A. Solla, T. K. Leen, and K. R. Muller, editors, Proceedings of Neural Information Processing Systems. MIT Press, November 1999. http://citeseer.ist.psu.edu/vaithyanathan99generalized.html More
@misc{ vaithyanathan99generalized,
author = "S. Vaithyanathan and B. Dom",
title = "Generalized model selection for unsupervised learning in high dimensions",
text = "Shivakumar Vaithyanathan and Byron Dom. Generalized model selection for
unsupervised learning in high dimensions. In S. A. Solla, T. K. Leen, and
K. R. Muller, editors, Proceedings of Neural Information Processing Systems.
MIT Press, November 1999.",
year = "1999",
url = "citeseer.ist.psu.edu/vaithyanathan99generalized.html" }
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13 Model Selection in Unsupervised Learning with Applications t.. (context) - Vaithyanathan, Dom - 1998
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3 Clustering images using relative entropy for efficient retri.. - Iyengar - 1998
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