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Generalized Low-Rank Approximations

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).

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6708
Date15 January 2003
CreatorsSrebro, Nathan, Jaakkola, Tommi
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format10 p., 2061103 bytes, 911431 bytes, application/postscript, application/pdf
RelationAIM-2003-001

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