An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Overview of the second text retrieval conference (TREC-2)
TREC-2 Proceedings of the second conference on Text retrieval conference
Communications of the ACM
ACM Computing Surveys (CSUR)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
BoostMap: An Embedding Method for Efficient Nearest Neighbor Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Counting Distance Permutations
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Effective Proximity Retrieval by Ordering Permutations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate similarity search in metric spaces using inverted files
Proceedings of the 3rd international conference on Scalable information systems
Approximate similarity search: A multi-faceted problem
Journal of Discrete Algorithms
Metric Index: An Efficient and Scalable Solution for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Speeding Up Permutation Based Indexing with Indexing
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Indexing inexact proximity search with distance regression in pivot space
Proceedings of the Third International Conference on SImilarity Search and APplications
On the least cost for proximity searching in metric spaces
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
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We aim at reducing the number of distance computations as much as possible in the inexact indexing schemes which sort the objects according to some promise values. To achieve this aim, we propose a new probability-based indexing scheme which can be applied to any inexact indexing scheme that uses the promise values. Our scheme (1) uses the promise values obtained from any inexact scheme to compute the new probability-based promise values. In order to estimate the new promise values, we (2) use the object-specific parameters in logistic regression and learn the parameters using MAP (Maximum a Posteriori) estimation. We also propose a technique which (3) speeds up learning the parameters using the promise values. We applied our scheme to the standard pivot-based scheme and the permutation-based scheme, and evaluated them using various kinds of datasets from the Metric Space Library. The results showed that our scheme improved the conventional schemes, in all cases.