Faster and Better Solution to Embed Lp Metrics by Tree Metrics

Author:

Zeng Yuxiang1,Tong Yongxin2,Chen Lei1

Affiliation:

1. The Hong Kong University of Science and Technology, Hong Kong SAR, China

2. Beihang University, Beijing, China

Funder

Hong Kong ITC ITF grants

Microsoft Research Asia Collaborative Research Grant

the Hong Kong RGC Theme-based project TRS

HKUST-Webank joint research lab grants

National Key Research and Development Program of China Grant

HKUST-NAVER/LINE AI Lab

HKUST Global Strategic Partnership Fund (2021 SJTU-HKUST)

the Hong Kong RGC CRF Project

Guangdong Basic and Applied Basic Research Foundation

the National Science Foundation of China (NSFC) under Grant

the Hong Kong RGC RIF Project

China NSFC

the State Key Laboratory of Software Development Environment Open Funding

the Hong Kong RGC GRF Project

the Hong Kong RGC AOE Project

Didi-HKUST joint research lab

Publisher

ACM

Reference66 articles.

1. 2021. Didi Chuxing. Retrieved Oct 21 2021 from https://www.didiglobal.com/ 2021. Didi Chuxing. Retrieved Oct 21 2021 from https://www.didiglobal.com/

2. 2021. Foursquare. Retrieved Oct 21 2021 from https://foursquare.com/ 2021. Foursquare. Retrieved Oct 21 2021 from https://foursquare.com/

3. 2021. UCAR Inc. Retrieved Oct 21 2021 from https://www.10101111.com/ 2021. UCAR Inc. Retrieved Oct 21 2021 from https://www.10101111.com/

4. Ahmed Abdelkader , Sunil Arya , Guilherme Dias da Fonseca, and David M. Mount . 2019 . Approximate Nearest Neighbor Searching with Non-Euclidean and Weighted Distances. In SODA. 355--372. Ahmed Abdelkader, Sunil Arya, Guilherme Dias da Fonseca, and David M. Mount. 2019. Approximate Nearest Neighbor Searching with Non-Euclidean and Weighted Distances. In SODA. 355--372.

5. Ittai Abraham Yair Bartal and Ofer Neiman. 2006. Advances in metric embedding theory. In STOC. 271--286. Ittai Abraham Yair Bartal and Ofer Neiman. 2006. Advances in metric embedding theory. In STOC. 271--286.

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1. LiteHST: A Tree Embedding based Method for Similarity Search;Proceedings of the ACM on Management of Data;2023-05-26

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