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dc.contributor.authorYing, Yiming
dc.contributor.authorPeng, Li
dc.date.accessioned2013-07-22T13:51:58Z
dc.date.issued2012
dc.description.abstractThe main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning approach called DML-eig which is shown to be equivalent to a well-known eigenvalue optimization problem called minimizing the maximal eigenvalue of a symmetric matrix (Overton, 1988; Lewis and Overton, 1996). Moreover, we formulate LMNN (Weinberger et al., 2005), one of the state-of-the-art metric learning methods, as a similar eigenvalue optimization problem. This novel framework not only provides new insights into metric learning but also opens new avenues to the design of efficient metric learning algorithms. Indeed, first-order algorithms are developed for DML-eig and LMNN which only need the computation of the largest eigenvector of a matrix per iteration. Their convergence characteristics are rigorously established. Various experiments on benchmark data sets show the competitive performance of our new approaches. In addition, we report an encouraging result on a difficult and challenging face verification data set called Labeled Faces in the Wild (LFW).en_GB
dc.identifier.citationVol. 13, pp. 1 - 26en_GB
dc.identifier.urihttp://hdl.handle.net/10871/11881
dc.language.isoenen_GB
dc.publisherMicrotome Publishingen_GB
dc.relation.urlhttp://jmlr.org/en_GB
dc.subjectmetric learningen_GB
dc.subjectconvex optimizationen_GB
dc.subjectsemi-definite programmingen_GB
dc.subjectfirst-order methodsen_GB
dc.subjecteigenvalue optimizationen_GB
dc.subjectmatrix factorizationen_GB
dc.subjectface verificationen_GB
dc.titleDistance Metric Learning with Eigenvalue Optimizationen_GB
dc.typeArticleen_GB
dc.date.available2013-07-22T13:51:58Z
dc.identifier.issn1532-4435
dc.descriptionCopyright © 2012 Yiming Ying and Peng Li.en_GB
dc.identifier.eissn1533-7928
dc.identifier.journalJournal of Machine Learning Researchen_GB


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