Guochang Gu
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computer Networks and Communications top 10%
- Molecular Biology
- Control and Systems Engineering top 10%
- Co-authors
- Meiping SongHaibo LiuHualong YuJing ShenRubo ZhangHaiyan YangJing ZhaoJian Li
- Topics
- Reinforcement Learning in Robotics (7 papers)Image Retrieval and Classification Techniques (7 papers)Robotic Path Planning Algorithms (7 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Guochang Gu
68 papers receiving 492 citations
Peers
Comparison fields: 5 of 88
- Artificial Intelligence 216
- Computer Vision and Pattern Recognition 170
- Computer Networks and Communications 111
- Molecular Biology 67
- Control and Systems Engineering 63
Countries citing papers authored by Guochang Gu
This map shows the geographic impact of Guochang Gu'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 Guochang Gu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Guochang Gu more than expected).
Fields of papers citing papers by Guochang Gu
This network shows the impact of papers produced by Guochang Gu. 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 Guochang Gu. The network helps show where Guochang Gu may publish in the future.
Co-authorship network of co-authors of Guochang Gu
This figure shows the co-authorship network connecting the top 25 collaborators of Guochang Gu. A scholar is included among the top collaborators of Guochang Gu 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 Guochang Gu. Guochang Gu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | Integrating K-means and non-negative matrix factorization to ensemble document clustering | 1 |
| 3 | Spectral clustering algorithms for document cluster ensemble problem | 3 |
| 4 | 1 | |
| 5 | 9 | |
| 6 | 2 | |
| 7 | A bidirectional 2-D linear discriminant analysis algorithm based on an adaptively weighted function | 1 |
| 8 | Discovery of data mining services based on domain ontology | 1 |
| 9 | 1 | |
| 10 | 7 | |
| 11 | 3 | |
| 12 | Research and design of hard real-time Linux kernel | 1 |
| 13 | 1 | |
| 14 | 9 | |
| 15 | 1 | |
| 16 | 1 | |
| 17 | 0 | |
| 18 | 5 | |
| 19 | A NEW MULTI-AGENT REINFORCEMENT LEARNING ALGORITHM AND ITS APPLICATION TO MULTI-ROBOT COOPERATION TASKS | 0 |
| 20 | 0 |
About Guochang Gu
Guochang Gu is a scholar working on Software, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 81 papers that have together received 546 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Image Retrieval and Classification Techniques (7 papers) and Robotic Path Planning Algorithms (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (170 citations), Software (29 citations) and Artificial Intelligence (216 citations). Guochang Gu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Meiping Song, Haibo Liu, Hualong Yu, Jing Shen, Rubo Zhang, Haiyan Yang, Jing Zhao, Jing Shen, Jian Li and Guoyin Zhang. Their work appears in journals such as Applied Physics Letters, Expert Systems with Applications and Solid State Communications.
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.