Pei Yang

2.0k citations
118 papers · 1.5k · h-index 19

Impact in

Papers in

Pei Yang

104 papers receiving 1.4k citations

Peers

Pei Yang
Comparison fields: 5 of 152
  • Pharmaceutical Science 225
  • Biomaterials 180
  • Health Informatics 18
  • Computational Mathematics 8
  • Computer Vision and Pattern Recognition 276
Replace Rakesh Gupta with:
Rakesh Gupta India
Jiahua Dong China
Cheng Chen China
Fengping An China
Mohd Asif Shah India
Guoqing Wang China
Peng Cheng China
Wenjuan Jia China
Xiangjun Zhao China
Zijian Wang China
Pei Yang relative to Rakesh Gupta India Rakesh Gupta's profile →
Citations per field
00.5×4.5×
Rakesh Gupta · 1×
Citations per year

Countries citing papers authored by Pei Yang

Since Specialization
Citations

This map shows the geographic impact of Pei Yang'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 Pei Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pei Yang more than expected).

Fields of papers citing papers by Pei Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pei Yang. 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 Pei Yang. The network helps show where Pei Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Pei Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Pei Yang Line = papers co-authored together Pei Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 118 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015201
2 2016128
3 2016128
4 201488
5 201974
6 202064
7 201647
8 202242
9
Multi-view discriminant transfer learning
201339
10 202235
11 202034
12 201431
13 202227
14 202125
15 202222
16 201720
17 201419
18 202218
19 202118
20 200817

About Pei Yang

Pei Yang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Molecular Biology and Electrical and Electronic Engineering, having authored 118 papers that have together received 1.5k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (16 papers), Face and Expression Recognition (12 papers), Text and Document Classification Technologies (9 papers), Topic Modeling (7 papers), Machine Learning and ELM (6 papers), Sparse and Compressive Sensing Techniques (5 papers), Complex Network Analysis Techniques (5 papers) and Multimodal Machine Learning Applications (5 papers). The work is most often cited by research in Pharmaceutical Science (225 citations), Biomaterials (180 citations), Health Informatics (18 citations), Computational Mathematics (8 citations) and Computer Vision and Pattern Recognition (276 citations). Pei Yang has collaborated with scholars based in China, United States and Qatar. Frequent co-authors include Limei Han, Jianxin Wang, Jing Qin, Jianyong Sheng, Ruixiang Li, Chunlin Chen, Wei Gao, Guihua Wen, Jingrui He and Lihong Wu. Their work appears in journals such as Pattern Recognition, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, Scientific Reports and ACM Transactions on Knowledge Discovery from Data.

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.

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