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Abstract: We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and e#cient (EM) algorithm for solving weighted low-rank approximation problems, which, unlike their unweighted version, do not admit a closedform solution in general. We analyze, in addition, the nature of locally optimal solutions that arise in this context, demonstrate the utility of accommodating the weights in reconstructing the underlying low-rank representation, and... (Update)
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BibTeX entry: (Update)
Nathan Srebro and Tommi Jaakkola. Weighted low rank approximation. In 20th International Conference on Machine Learning, 2003. http://citeseer.ist.psu.edu/srebro03weighted.html More
@misc{ srebro03weighted,
author = "N. Srebro and T. Jaakkola",
title = "Weighted low rank approximation",
text = "Nathan Srebro and Tommi Jaakkola. Weighted low rank approximation. In 20th
International Conference on Machine Learning, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/srebro03weighted.html" }
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