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Fast Nearest Neighbor Retrieval for Bregman Divergences
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. We present a data structure enabling efficient NN retrieval for bregman divergences. The family of bregman divergences includes many popular dissimilarity measures including KL-divergence (relative entropy), Mahalanobis distance, and Itakura-Saito divergence. These divergences present a challenge for efficient NN retrieval because they are not, in general, metrics, for which most NN data structures are designed. The data structure introduced in this work shares the same basic structure as the popular metric ball tree, but employs convexity properties of bregman divergences in place of the triangle inequality. Experiments demonstrate speedups over brute-force search of up to several orders of magnitude.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
