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%.

引用

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页码：89 / 97

页数：9

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