Hong Chen
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Electrical and Electronic Engineering
- Computational Mechanics top 5%
- Plant Science
- Co-authors
- Luoqing LiYulong WangJong‐Gwan YookEakhwan SongYong–June ShinE.J. PowersJ.B. ParkJiangtao Peng
- Topics
- Face and Expression Recognition (32 papers)Sparse and Compressive Sensing Techniques (27 papers)Blind Source Separation Techniques (11 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputational MechanicsFluid Flow and Transfer Processes
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Hong Chen
117 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 137
- Computer Vision and Pattern Recognition 574
- Artificial Intelligence 368
- Electrical and Electronic Engineering 272
- Computational Mechanics 260
- Plant Science 137
Countries citing papers authored by Hong Chen
This map shows the geographic impact of Hong Chen'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 Hong Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hong Chen more than expected).
Fields of papers citing papers by Hong Chen
This network shows the impact of papers produced by Hong Chen. 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 Hong Chen. The network helps show where Hong Chen may publish in the future.
Co-authorship network of co-authors of Hong Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Hong Chen. A scholar is included among the top collaborators of Hong Chen 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 Hong Chen. Hong Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 18 | |
| 6 | 70 | |
| 7 | 1 | |
| 8 | 17 | |
| 9 | 7 | |
| 10 | 2 | |
| 11 | 7 | |
| 12 | 29 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | Group Sparse Additive Machine | 18 |
| 16 | Error analysis of generalized nyström kernel regression | 4 |
| 17 | 5 | |
| 18 | Study on crucian and cyprinoid species identification method based on machine vision | 1 |
| 19 | The Characteristic and Improved Arithmetic in the Multi-Line Detection Based on Hough Transformation | 1 |
| 20 | 11 |
About Hong Chen
Hong Chen is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Computational Mathematics, having authored 136 papers that have together received 1.6k indexed citations. Recurring topics across this work include Face and Expression Recognition (32 papers), Sparse and Compressive Sensing Techniques (27 papers) and Blind Source Separation Techniques (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (574 citations), Computational Mechanics (260 citations) and Fluid Flow and Transfer Processes (75 citations). Hong Chen has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Luoqing Li, Yulong Wang, Jong‐Gwan Yook, Eakhwan Song, Yong–June Shin, E.J. Powers, J.B. Park, Jiangtao Peng, Zhenqiu Liu and Zheng Xu. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.