Accelerating exact nearest neighbor search in high dimensional Euclidean space via block vectors

International Journal of Intelligent SystemsVolume 37, Issue 2 p. 1697-1722 RESEARCH ARTICLE Accelerating exact nearest neighbor search in high dimensional Euclidean space via block vectors Haowen Zhang, Haowen Zhang orcid.org/0000-0001-8946-1273 College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorYabo Dong, Corresponding Author Yabo Dong dongyb@zju.edu.cn orcid.org/0000-0003-2356-5875 College of Computer Science and Technology, Zhejiang University, Hangzhou, China Correspondence Yabo Dong, College of Computer Science and Technology, Zhejiang University, No. 38, Zhe-Da Road, Hangzhou 310027, China. Email: dongyb@zju.edu.cnSearch for more papers by this authorDuanqing Xu, Duanqing Xu orcid.org/0000-0001-5311-3728 College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this author Haowen Zhang, Haowen Zhang orcid.org/0000-0001-8946-1273 College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorYabo Dong, Corresponding Author Yabo Dong dongyb@zju.edu.cn orcid.org/0000-0003-2356-5875 College of Computer Science and Technology, Zhejiang University, Hangzhou, China Correspondence Yabo Dong, College of Computer Science and Technology, Zhejiang University, No. 38, Zhe-Da Road, Hangzhou 310027, China. Email: dongyb@zju.edu.cnSearch for more papers by this authorDuanqing Xu, Duanqing Xu orcid.org/0000-0001-5311-3728 College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this author First published: 01 October 2021 https://doi.org/10.1002/int.22692Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The nearest neighbor search is an essential operation for many computer vision, data mining, and machine learning problems. Since it is so widely used, the nearest neighbor search should be as fast as possible. This paper explores lower bound-based approaches to speed up the exact nearest neighbor search in high dimensional Euclidean space. We compute the lower bound of Euclidean Distance by using the block vectors and Cauchy–Schwartz inequality. The proposed lower bound is calculated efficiently and is close to the real Euclidean Distance. Besides, the preprocessing step of the proposal has linear time complexity. Given a query, during the procedure of identifying the nearest neighbor, our method can eliminate many expensive actual distance computations using the lower bound to approximate Euclidean Distance. In addition, we develop a multilevel lower bound strategy, which calculates the lower bound step by step and utilizes the multistep filtering mechanism to improve the searching process further. Theoretical analysis is provided to show that the proposals can guarantee to obtain the same result as the brute-force search. Comprehensive experiments on 16 public data sets collected from various domains demonstrate that our approach performs well in finding the exact nearest neighbor compared to related competitors. The experimental results also illustrate that the multilevel lower bound strategy is effective. Volume37, Issue2February 2022Pages 1697-1722 RelatedInformation