Learning Dissimilarities by Ranking: From SDP to QP

author: Hua Ouyang, College of Computing, Georgia Institute of Technology
published: Aug. 29, 2008,   recorded: July 2008,   views: 3205


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We consider the problem of learning dissimilarities between points via formulations which preserve a specified ordering between points rather than the numerical values of the dissimilarities. Dissimilarity ranking (d-ranking) learns from instances like "A is more similar to B than C is to D" or "The distance between E and F is larger than that between G and H". Three formulations of d-ranking problems are presented and new algorithms are presented for two of them, one by semidefinite programming (SDP) and one by quadratic programming (QP). Among the novel capabilities of these approaches are out-of-sample prediction and scalability to large problems.

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Comment1 Pastro Park, August 31, 2008 at 6:38 a.m.:

I can't hear ant sound.
Is it my computer problem
or your casting problem?

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