Nonlinear Learning using Local Coordinate Coding
Impact in
Classified as
- Authors
- Kai YuTong ZhangYihong Gong
- Journal
- Rare & Special e-Zone (The Hong Kong University of Science and Technology)
In The Last Decade
doi.org/w4097675 →Countries where authors are citing Nonlinear Learning using Local Coordinate Coding
This map shows the geographic impact of Nonlinear Learning using Local Coordinate Coding. 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 Nonlinear Learning using Local Coordinate Coding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nonlinear Learning using Local Coordinate Coding more than expected).
Fields of papers citing Nonlinear Learning using Local Coordinate Coding
This network shows the impact of Nonlinear Learning using Local Coordinate Coding. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Nonlinear Learning using Local Coordinate Coding.
About Nonlinear Learning using Local Coordinate Coding
This paper, published in 2009, received 520 indexed citations . Written by Kai Yu, Tong Zhang and Yihong Gong covering the research area of Media Technology, Artificial Intelligence and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (459 citations), Artificial Intelligence (127 citations), Computational Mechanics (108 citations), Media Technology (108 citations) and Aerospace Engineering (33 citations). Published in Rare & Special e-Zone (The Hong Kong University of Science and Technology).
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/w4097675.