Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6708
Title: Generalized Low-Rank Approximations
Keywords: AI
svd pca
Issue Date: 9-Oct-2013
Description: We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM) algorithm for solving {\\em weighted} low rank approximation problems, which, unlike simple matrix factorization problems, do not admit a closed form 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 extend the formulation to non-Gaussian noise models such as classification (collaborative filtering).
URI: http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers: AIM-2003-001
http://hdl.handle.net/1721.1/6708
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