@inproceedings{2009-W-BregmanTrees-EuroCG
, author={Paolo Piro and Frank Nielsen and Michel Barlaud}
, title={Tailored Bregman ball trees for effective nearest neighbors}
, booktitle={European Workshop on Computational Geometry (EuroCG)}
, month={March}
, year={2009}
, address={LORIA, Nancy, France}
, publisher={IEEE}
, 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]}
}