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Abstract: We suggest a nonparametric framework for unsupervised learning of projection models in terms of density estimation on quantized sample spaces. The objective is not to optimally reconstruct the data but instead the quantizer is chosen to optimally reconstruct the density of the data. For the resulting quantizing density estimator (QDE) we present a general method for parameter estimation and model selection. We show how projection sets which correspond to traditional unsupervised methods... (Update)
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BibTeX entry: (Update)
P. Meinicke and H. Ritter. Quantizing density estimators. In T. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14 (NIPS), volume 14, pages 825--832, Cambridge, MA, 2002. MIT Press. http://citeseer.ist.psu.edu/meinicke01quantizing.html More
@misc{ meinicke02quantizing,
author = "P. Meinicke and H. Ritter",
title = "Quantizing density estimators",
text = "P. Meinicke and H. Ritter. Quantizing density estimators. In T. Dietterich,
S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing
Systems 14 (NIPS), volume 14, pages 825--832, Cambridge, MA, 2002. MIT Press.",
year = "2002",
url = "citeseer.ist.psu.edu/meinicke01quantizing.html" }
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