Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/70243
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dc.contributor.authorShen, C.-
dc.contributor.authorKim, J.-
dc.contributor.authorWang, L.-
dc.contributor.authorVan Den Hengel, A.-
dc.date.issued2012-
dc.identifier.citationJournal of Machine Learning Research, 2012; 13(0):1007-1036-
dc.identifier.issn1532-4435-
dc.identifier.issn1533-7928-
dc.identifier.urihttp://hdl.handle.net/2440/70243-
dc.description.abstractThe success of many machine learning and pattern recognition methods relies heavily upon the identification of on an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive semidefinite. Semidefinite programming is often used to enforce this constraint, but does not scale well and not easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear combination of trace-one rank-one matrices. BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare favorably to those state-of-the-art methods in terms of classification accuracy and running time.-
dc.description.statementofresponsibilityChunhua Shen, Junae Kim, Lei Wang, Anton van den Hengel-
dc.language.isoen-
dc.publisherMIT Press-
dc.rights© 2012 Chunhua Shen, Junae Kim, Lei Wang, and Anton van den Hengel.-
dc.subjectMahalanobis distance-
dc.subjectsemidefinite programming-
dc.subjectcolumn generation-
dc.subjectboosting-
dc.subjectLagrange duality-
dc.subjectlarge margin nearest neighbor.-
dc.titlePositive semidefinite metric learning using boosting-like algorithms-
dc.typeJournal article-
pubs.publication-statusPublished-
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]-
Appears in Collections:Aurora harvest
Computer Science publications

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