Multi-probe LSH: efficient indexing for high-dimensional similarity search
Impact in
Classified as
- Authors
- Qin LvWilliam JosephsonZhe WangMoses CharikarKai Li
- Journal
- Very Large Data Bases
In The Last Decade
doi.org/w9365038 →Countries where authors are citing Multi-probe LSH: efficient indexing for high-dimensional similarity search
This map shows the geographic impact of Multi-probe LSH: efficient indexing for high-dimensional similarity search. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Multi-probe LSH: efficient indexing for high-dimensional similarity search with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Multi-probe LSH: efficient indexing for high-dimensional similarity search more than expected).
Fields of papers citing Multi-probe LSH: efficient indexing for high-dimensional similarity search
This network shows the impact of Multi-probe LSH: efficient indexing for high-dimensional similarity search. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Multi-probe LSH: efficient indexing for high-dimensional similarity search.
About Multi-probe LSH: efficient indexing for high-dimensional similarity search
This paper, published in 2007, received 422 indexed citations . Written by Qin Lv, William Josephson, Zhe Wang, Moses Charikar and Kai Li covering the research area of Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (331 citations), Signal Processing (109 citations), Artificial Intelligence (108 citations), Computer Networks and Communications (99 citations) and Aerospace Engineering (73 citations). Published in Very Large Data Bases.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w9365038.