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iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
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On approximating the smallest enclosing Bregman Balls
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Fast nearest neighbor retrieval for bregman divergences
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Bridging the Gap: Query by Semantic Example
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Video Annotation Based on Kernel Linear Neighborhood Propagation
IEEE Transactions on Multimedia
Nearest neighbor search: algorithmic perspective
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An efficient algorithm for reverse furthest neighbors query with metric index
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Efficient and effective similarity search over probabilistic data based on earth mover's distance
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Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient exact edit similarity query processing with the asymmetric signature scheme
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Approximate bregman near neighbors in sublinear time: beyond the triangle inequality
Proceedings of the twenty-eighth annual symposium on Computational geometry
Asymmetric signature schemes for efficient exact edit similarity query processing
ACM Transactions on Database Systems (TODS)
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In this paper, we examine the problem of indexing over non-metric distance functions. In particular, we focus on a general class of distance functions, namely Bregman Divergence [6], to support nearest neighbor and range queries. Distance functions such as KL-divergence and Itakura-Saito distance, are special cases of Bregman divergence, with wide applications in statistics, speech recognition and time series analysis among others. Unlike in metric spaces, key properties such as triangle inequality and distance symmetry do not hold for such distance functions. A direct adaptation of existing indexing infrastructure developed for metric spaces is thus not possible. We devise a novel solution to handle this class of distance measures by expanding and mapping points in the original space to a new extended space. Subsequently, we show how state-of-the-art tree-based indexing methods, for low to moderate dimensional datasets, and vector approximation file (VA-file) methods, for high dimensional datasets, can be adapted on this extended space to answer such queries efficiently. Improved distance bounding techniques and distribution-based index optimization are also introduced to improve the performance of query answering and index construction respectively, which can be applied on both the R-trees and VA files. Extensive experiments are conducted to validate our approach on a variety of datasets and a range of Bregman divergence functions.