Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
The Journal of Machine Learning Research
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Clustering with Bregman Divergences
The Journal of Machine Learning Research
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Sketching information divergences
COLT'07 Proceedings of the 20th annual conference on Learning theory
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Efficient shape indexing using an information theoretic representation
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Bridging the Gap: Query by Semantic Example
IEEE Transactions on Multimedia
Similarity search on Bregman divergence: towards non-metric indexing
Proceedings of the VLDB Endowment
Bregman vantage point trees for efficient nearest neighbor queries
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
What is the complexity of a network? the heat flow-thermodynamic depth approach
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Transfer latent variable model based on divergence analysis
Pattern Recognition
Approximate bregman near neighbors in sublinear time: beyond the triangle inequality
Proceedings of the twenty-eighth annual symposium on Computational geometry
Towards a universal tracking database
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Hi-index | 0.00 |
We present a data structure enabling efficient nearest neighbor (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.