Gui–Bo Ye

628 citations
9 papers · 427 indexed · h-index 8

Impact in

Papers in

Gui–Bo Ye

9 papers receiving 405 citations

Peers

Gui–Bo Ye
Comparison fields: 5 of 65
  • Computer Vision and Pattern Recognition 217
  • Computational Mathematics 6
  • Computational Mechanics 204
  • Statistics and Probability 53
  • Media Technology 41
Replace Chengda Yang with:
Chengda Yang United States
Vincent Duval France
Caroline Chaux France
Caroline Chaux France
Bernhard Schmitzer Germany
Martin Welk Germany
Ursula Molter Argentina
Silvia Bonettini Italy
Ajil Jalal United States
Elisabeth Rouy France
Gui–Bo Ye relative to Chengda Yang United States Chengda Yang's profile →
Citations per field
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Chengda Yang · 1×
Citations per year

Countries citing papers authored by Gui–Bo Ye

Since Specialization
Citations

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

Fields of papers citing papers by Gui–Bo Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gui–Bo Ye. 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 Gui–Bo Ye. The network helps show where Gui–Bo Ye may publish in the future.

Co-authorship network

The 9 scholars most cited alongside Gui–Bo Ye, 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 Gui–Bo Ye Line = papers co-authored together Gui–Bo Ye links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 201643
2 2013199
3 201311
4 201212
5
Efficient variable selection in support vector machines via the alternating direction method of multipliers
201131
6 20105
7 201067
8 200726
9 200733

About Gui–Bo Ye

Gui–Bo Ye is a scholar working on Computational Mechanics, Statistics and Probability, Computer Vision and Pattern Recognition, Applied Mathematics and Mathematical Physics, having authored 9 papers that have together received 427 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (6 papers), Image and Signal Denoising Methods (2 papers), Bayesian Methods and Mixture Models (2 papers), Mathematical Analysis and Transform Methods (2 papers), Face and Expression Recognition (2 papers), Statistical Methods and Inference (2 papers), Control Systems and Identification (2 papers) and Neural Networks and Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (217 citations), Computational Mathematics (6 citations), Computational Mechanics (204 citations), Statistics and Probability (53 citations) and Media Technology (41 citations). Gui–Bo Ye has collaborated with scholars based in United States, Hong Kong and China. Frequent co-authors include Jian‐Feng Cai, Zuowei Shen, Hui Ji, Xiaohui Xie, Ding‐Xuan Zhou, Xiaobo Qu, Weiyu Xu, Xiaohui Xie and Qing Nie. Their work appears in journals such as Applied and Computational Harmonic Analysis, Machine Learning, Computational Statistics & Data Analysis, PLoS ONE and Advances in Computational Mathematics.

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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