Generalized Low-Rank Approximations

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6708

Full metadata record

DC Field	Value	Language
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	-
Appears in Collections:	MIT Items

Files in This Item:

There are no files associated with this item.

DSpace JSPUI