Tailored Bregman Ball Trees for Effective Nearest Neighbors

Abstract : Nearest Neighbor (NN) search is a crucial tool that remains critical in many challenging applications of computational geometry (e.g., surface reconstruction, clustering) and computer vision (e.g., image and information retrieval, classification, data mining). We present an effective Bregman ball tree [5] (Bb-tree) construction algorithm that adapts locally its internal node degrees to the inner geometric characteristics of the data-sets. Since symmetric measures are usually preferred for applications in content-based information retrieval, we furthermore extend the Bb-tree to the case of symmetrized Bregman divergences. Exact and approximate NN search experiments using high-dimensional real-world data-sets illustrate that our method improves significantly over the state of the art [5], sometimes by an order of magnitude.
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Communication dans un congrès
Proceedings of the 25th European Workshop on Computational Geometry (EuroCG), Mar 2009, Brussels, Belgium. pp.29-32, 2009
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Frank Nielsen, Paolo Piro, Michel Barlaud. Tailored Bregman Ball Trees for Effective Nearest Neighbors. Proceedings of the 25th European Workshop on Computational Geometry (EuroCG), Mar 2009, Brussels, Belgium. pp.29-32, 2009. 〈hal-00382782〉

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