Cover Trees Revisited: Exploiting Unused Distance and Direction Information
The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this paper, we revisit cover trees f...
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| Vydáno v: | IEEE transactions on knowledge and data engineering Ročník 35; číslo 11; s. 1 - 16 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.11.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1041-4347, 1558-2191 |
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| Abstract | The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this paper, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (I) range list, (II) quadrant information, and (III) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants. |
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| AbstractList | The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this paper, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (I) range list, (II) quadrant information, and (III) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants. The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this article, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (i) range list, (ii) quadrant information, and (iii) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and [Formula Omitted] nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants. |
| Author | Zhao, Kaiqi Wang, Zhi-Jie Quan, Zhe Nie, Mengdie Yao, Bin |
| Author_xml | – sequence: 1 givenname: Zhi-Jie orcidid: 0000-0002-6865-7899 surname: Wang fullname: Wang, Zhi-Jie organization: College of Computer Science, Chongqing University, China – sequence: 2 givenname: Mengdie surname: Nie fullname: Nie, Mengdie organization: Pinduoduo Company – sequence: 3 givenname: Kaiqi orcidid: 0000-0002-0984-1629 surname: Zhao fullname: Zhao, Kaiqi organization: Department of Computer Science, University of Auckland, New Zealand – sequence: 4 givenname: Zhe orcidid: 0000-0003-2669-9190 surname: Quan fullname: Quan, Zhe organization: College of Computer Science, Hunan University, Changsha, China – sequence: 5 givenname: Bin orcidid: 0000-0002-6478-4209 surname: Yao fullname: Yao, Bin organization: Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China |
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| SubjectTerms | Algorithms Approximation algorithms Computed tomography Computer science Cover tree data mining Data search data structure and algorithms Data structures machine learing Navigation Nearest neighbor methods Runtime Search algorithms Synthetic data Trigonometric functions |
| Title | Cover Trees Revisited: Exploiting Unused Distance and Direction Information |
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