Abstract
Similarity searching consists of retrieving the most similar elements in a database. This is a central problem in many real applications, and it becomes intractable when a big database is used. A way to overcome this problem is by getting a few objects as a promissory candidate list of being part of the answer. In this paper, the most relevant and efficient algorithms for high dimensional spaces based on the permutations-technique are compared. Permutation-based algorithm is related to make a permutation of some special objects that allows us to organize the space of the elements in a database. One of the indexes related uses a complete permutation, and the second one utilizes a small part of the permutation and an inverted index.
Our research is focussed on two proposed ideas: the first consists in using a similar inverted index only with less information per object and computing the candidate list in a different way; and the second consists in changing a parameter during querying time in order to achieve a better prediction of the nearest neighbors. Our experiments show that our proposals do serve for implementing a better predictor and that the nearest neighbor can be found computing up to 45% fewer distances per query.
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Figueroa, K., Reyes, N., Camarena-Ibarrola, A. (2020). Candidate List Obtained from Metric Inverted Index for Similarity Searching. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_3
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