ACM Computing Surveys (CSUR)
SIAM Journal on Discrete Mathematics
Effective Proximity Retrieval by Ordering Permutations
IEEE Transactions on Pattern Analysis and Machine Intelligence
MiPai: Using the PP-Index to Build an Efficient and Scalable Similarity Search System
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
On locality sensitive hashing in metric spaces
Proceedings of the Third International Conference on SImilarity Search and APplications
Efficient group of permutants for proximity searching
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Proximity searching in high dimensional spaces with a proximity preserving order
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
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Proximity searching consists in retrieving the most similar objects to a given query. This kind of searching is a basic tool in many fields of artificial intelligence, because it can be used as a search engine to solve problems like $kN\!N$ searching. A common technique to solve proximity queries is to use an index. In this paper, we show a variant of the permutation based index, which, in his original version, has a great predicting power about which are the objects worth to compare with the query (avoiding the exhaustive comparison). We have noted that when two permutants are close, they can produce small differences in the order in which objects are revised, which could be responsible of finding the true answer or missing it. In this paper we pretend to mitigate this effect. As a matter of fact, our technique allows us both to reduce the index size and to improve the query cost up to 30%.