Shao-Yuan Li
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 10%
- Media Technology top 5%
- Computer Science Applications top 5%
- Co-authors
- Zhi‐Hua ZhouYuan JiangSheng-Jun HuangSongcan ChenNitesh V. ChawlaDaizong DingMi ZhangJie Tang
- Topics
- Text and Document Classification Technologies (7 papers)Mobile Crowdsensing and Crowdsourcing (6 papers)Domain Adaptation and Few-Shot Learning (5 papers)
- Journals
- IEEE Transactions on Geoscience and Remote SensingIEEE Transactions on Knowledge and Data EngineeringNeural Networks
- Partner nations
- ChinaUnited StatesSwitzerland
In The Last Decade
Shao-Yuan Li
16 papers receiving 512 citations
Hit Papers
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 331
- Computer Vision and Pattern Recognition 299
- Information Systems 70
- Media Technology 67
- Computer Science Applications 51
Countries citing papers authored by Shao-Yuan Li
This map shows the geographic impact of Shao-Yuan Li's research. 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 Shao-Yuan Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shao-Yuan Li more than expected).
Fields of papers citing papers by Shao-Yuan Li
This network shows the impact of papers produced by Shao-Yuan Li. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shao-Yuan Li. The network helps show where Shao-Yuan Li may publish in the future.
Co-authorship network of co-authors of Shao-Yuan Li
This figure shows the co-authorship network connecting the top 25 collaborators of Shao-Yuan Li. A scholar is included among the top collaborators of Shao-Yuan Li based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shao-Yuan Li. Shao-Yuan Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 1 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 30 | |
| 13 | 15 | |
| 14 | 18 | |
| 15 | 35 | |
| 16 | 15 | |
| 17 | 38 | |
| 18 | Partial Multi-View Clusteringbreakdown → | 327 |
| 19 | 16 |
About Shao-Yuan Li
Shao-Yuan Li is a scholar working on Computer Science Applications, Artificial Intelligence and Signal Processing, having authored 19 papers that have together received 517 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (7 papers), Mobile Crowdsensing and Crowdsourcing (6 papers) and Domain Adaptation and Few-Shot Learning (5 papers). The work is most often cited by research in Computational Mathematics (14 citations), Computer Vision and Pattern Recognition (299 citations) and Artificial Intelligence (331 citations). Shao-Yuan Li has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Zhi‐Hua Zhou, Yuan Jiang, Sheng-Jun Huang, Songcan Chen, Nitesh V. Chawla, Yuan Jiang, Daizong Ding, Mi Zhang, Jie Tang and Guoxiang Li. Their work appears in journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Knowledge and Data Engineering and Neural Networks.
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.