Shortening the Candidate List for Similarity Searching Using Inverted Index

被引：2
|
作者：
Figueroa, Karina [1 ]
Camarena-Ibarrola, Antonio [1 ]
Reyes, Nora [2 ]
机构：
[1] Univ Michoacana San Nicol prime Hidalgo, Morelia, Michoacan, Mexico
[2] Univ Nacl San Luis, Dept Informat, San Luis, Argentina
来源：
PATTERN RECOGNITION (MCPR 2021) | 2021年 / 12725卷
关键词：
D O I：
10.1007/978-3-030-77004-4_9
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Similarity searching consists of retrieving elements from a database that are closest to a given query. One strategy selects some elements as references and uses them to organize the whole database. With these reference points, it is possible to obtain a candidate list that contains the answer to the query and the rest of the database can be discarded for a while. In this article, a new strategy for reducing the candidate list is proposed. According to the experiments presented, it is possible to reduce the size of the list by up to 35%.
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页码：89 / 97
页数：9
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