Generalized Low-Rank Approximations

dc.creator	Srebro, Nathan
dc.creator	Jaakkola, Tommi
dc.date	2004-10-08T20:38:40Z
dc.date	2004-10-08T20:38:40Z
dc.date	2003-01-15
dc.date.accessioned	2013-10-09T02:46:31Z
dc.date.available	2013-10-09T02:46:31Z
dc.date.issued	2013-10-09
dc.identifier	AIM-2003-001
dc.identifier	http://hdl.handle.net/1721.1/6708
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.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).
dc.format	10 p.
dc.format	2061103 bytes
dc.format	911431 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-2003-001
dc.subject	AI
dc.subject	svd pca
dc.title	Generalized Low-Rank Approximations

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