Abstract
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%.
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Chávez, E., Figueroa, K., Navarro, G.: Proximity Searching in High Dimensional Spaces with a Proximity Preserving Order. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 405–414. Springer, Heidelberg (2005)
Chávez, E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 30(9), 1647–1658 (2009)
Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.: Searching in metric spaces. ACM Computing Surveys 33(3), 273–321 (2001)
Esuli, A.: Mipai: using the PP-Index to build an efficient and scalable similarity search system. In: Proc. 2nd Intl. Workshop on Similary Searching and Applications (SISAP 2009), pp. 146–148. IEEE Computer Society (2009)
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003)
Falchi, F., Kacimi, M., Mass, Y., Rabitti, F., Zezula, P.: Sapir: Scalable and distributed image searching. In: SAMT (Posters and Demos). CEUR Workshop Proceedings, vol. 300, pp. 11–12 (2007)
Figueroa Mora, K., Paredes, R.: Finding good permutants for proximity searching in metric spaces. In: Proc. 2010 Intl. Conf. on Information Security and Artificial Intelligenge (ISAI 2010), vol. 1, pp. 320–323. IEEE Computer Society Press (2010)
Figueroa Mora, K., Paredes, R., Rangel, R.: Efficient Group of Permutants for Proximity Searching. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds.) MCPR 2011. LNCS, vol. 6718, pp. 42–49. Springer, Heidelberg (2011)
Sadit, E., Chávez, E.: On locality sensitive hashing in metric spaces. In: Proc. 3rd Intl. Workshop on Similarity Search and Applications (SISAP 2010), pp. 67–74. ACM Press (2010)
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Figueroa Mora, K., Paredes, R. (2012). Compact and Efficient Permutations for Proximity Searching. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds) Pattern Recognition. MCPR 2012. Lecture Notes in Computer Science, vol 7329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31149-9_21
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DOI: https://doi.org/10.1007/978-3-642-31149-9_21
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