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Abstract: We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model that assumes the expression of observed high-dimensional binary data is driven by a small number of hidden binary sources combined via noisy-or units. The component analysis procedure is equivalent to learning of NOCA parameters. Since the classical EM formulation of the NOCA learning problem is intractable, we... (Update)
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BibTeX entry: (Update)
@misc{ tomas-journal,
author = "Tom Singliar Tomas",
title = "Journal of Machine Learning Research 7 (2006) 2189-2213 Submitted 1/06;
Revised 7/06; Published 10/06 Noisy-OR Component Analysis and its Application
to Link Analysis",
url = "citeseer.ist.psu.edu/760079.html" }
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