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dc.contributor.authorAthitsos, Vassilisen_US
dc.contributor.authorAlon, Jonien_US
dc.contributor.authorSclaroff, Stanen_US
dc.contributor.authorKollios, Georgeen_US
dc.date.accessioned2011-10-20T04:19:16Z
dc.date.available2011-10-20T04:19:16Z
dc.date.issued2004-04-07
dc.identifier.urihttps://hdl.handle.net/2144/1542
dc.description.abstractBoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.en_US
dc.description.sponsorshipU.S. National Science Foundation (IIS-0208876, IIS-0308213, IIS-03290009); U.S. Office of Naval Research (N00014-03-1-0108)en_US
dc.language.isoen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2004-014
dc.titleLearning Euclidean Embeddings for Indexing and Classificationen_US
dc.typeTechnical Reporten_US


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