Yonghao Li

1.1k total citations
25 papers, 757 citations indexed

About

Yonghao Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Yonghao Li has authored 25 papers receiving a total of 757 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 2 papers in Molecular Biology. Recurrent topics in Yonghao Li's work include Text and Document Classification Technologies (18 papers), Face and Expression Recognition (15 papers) and Image Retrieval and Classification Techniques (14 papers). Yonghao Li is often cited by papers focused on Text and Document Classification Technologies (18 papers), Face and Expression Recognition (15 papers) and Image Retrieval and Classification Techniques (14 papers). Yonghao Li collaborates with scholars based in China, United States and Canada. Yonghao Li's co-authors include Wanfu Gao, Liang Hu, Ping Zhang, Juncheng Hu, Tingting Chen, Wei Wang, Yi Zhu, Yizhi Liu, Chuan Chen and Tao Li and has published in prestigious journals such as PLoS ONE, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Yonghao Li

24 papers receiving 746 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yonghao Li China 16 536 445 131 110 78 25 757
Zhiling Cai China 9 191 0.4× 145 0.3× 35 0.3× 35 0.3× 30 0.4× 30 334
Jianhua Chen China 10 118 0.2× 98 0.2× 52 0.4× 20 0.2× 69 0.9× 57 321
Yuling Liu China 13 198 0.4× 225 0.5× 139 1.1× 17 0.2× 4 0.1× 73 564
Li Deng China 11 270 0.5× 67 0.2× 11 0.1× 78 0.7× 12 0.2× 39 543
S. Amirhassan Monadjemi Iran 13 83 0.2× 172 0.4× 16 0.1× 12 0.1× 9 0.1× 42 422
Connie Loggia Ramsey United States 8 362 0.7× 35 0.1× 71 0.5× 40 0.4× 97 1.2× 11 479
Kailash Shaw India 9 212 0.4× 61 0.1× 40 0.3× 50 0.5× 22 0.3× 38 391
E. G. Rajan India 10 81 0.2× 96 0.2× 28 0.2× 97 0.9× 14 0.2× 42 344
Naeem Radi United Kingdom 7 114 0.2× 180 0.4× 20 0.2× 5 0.0× 5 0.1× 11 376
Michele Merler United States 14 197 0.4× 393 0.9× 56 0.4× 23 0.2× 3 0.0× 35 597

Countries citing papers authored by Yonghao Li

Since Specialization
Citations

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

Fields of papers citing papers by Yonghao Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yonghao Li

This figure shows the co-authorship network connecting the top 25 collaborators of Yonghao Li. A scholar is included among the top collaborators of Yonghao 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 Yonghao Li. Yonghao Li is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Li, Yonghao, et al.. (2025). Exploring multi-label feature selection via feature and label information supplementation. Engineering Applications of Artificial Intelligence. 159. 111552–111552. 1 indexed citations
2.
Ma, Lin, Liang Hu, Yonghao Li, Weiping Ding, & Wanfu Gao. (2025). MI-MCF: A Mutual Information-Based Multilabel Causal Feature Selection. IEEE Transactions on Neural Networks and Learning Systems. 36(6). 9864–9878. 1 indexed citations
3.
Li, Yonghao, et al.. (2024). Multi-label feature selection with high-sparse personalized and low-redundancy shared common features. Information Processing & Management. 61(3). 103633–103633. 22 indexed citations
4.
Li, Yonghao, et al.. (2024). Fusion-enhanced multi-label feature selection with sparse supplementation. Information Fusion. 117. 102813–102813. 4 indexed citations
5.
Li, Yonghao, Tao Zhou, Kelei He, Yi Zhou, & Dinggang Shen. (2023). Multi-Scale Transformer Network With Edge-Aware Pre-Training for Cross-Modality MR Image Synthesis. IEEE Transactions on Medical Imaging. 42(11). 3395–3407. 26 indexed citations
6.
Li, Yonghao, Liang Hu, & Wanfu Gao. (2022). Label correlations variation for robust multi-label feature selection. Information Sciences. 609. 1075–1097. 32 indexed citations
7.
Zhang, Ping, Jiyao Sheng, Wanfu Gao, Juncheng Hu, & Yonghao Li. (2022). Multi-label feature selection method based on dynamic weight. Soft Computing. 26(6). 2793–2805. 7 indexed citations
8.
Hu, Liang, et al.. (2022). Feature-specific mutual information variation for multi-label feature selection. Information Sciences. 593. 449–471. 77 indexed citations
9.
Li, Yonghao, Liang Hu, & Wanfu Gao. (2022). Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation. Pattern Recognition. 134. 109120–109120. 40 indexed citations
10.
Liu, Zhijie, et al.. (2022). Vehicle Detection Based on Improved Yolov5s Algorithm. 7. 295–300. 1 indexed citations
11.
Li, Yonghao, Juncheng Hu, & Wanfu Gao. (2022). Robust multi-label feature selection with shared label enhancement. Knowledge and Information Systems. 64(12). 3343–3372. 8 indexed citations
12.
Li, Yonghao, Liang Hu, & Wanfu Gao. (2022). Multi-label feature selection via robust flexible sparse regularization. Pattern Recognition. 134. 109074–109074. 68 indexed citations
13.
Gao, Wanfu, Liang Hu, Yonghao Li, & Ping Zhang. (2021). Preserving Similarity and Staring Decisis for Feature Selection. IEEE Transactions on Artificial Intelligence. 2(6). 584–593. 17 indexed citations
14.
Gao, Wanfu, Yonghao Li, & Liang Hu. (2021). Multilabel Feature Selection With Constrained Latent Structure Shared Term. IEEE Transactions on Neural Networks and Learning Systems. 34(3). 1253–1262. 76 indexed citations
15.
Zhang, Ping, Wanfu Gao, Juncheng Hu, & Yonghao Li. (2021). A conditional-weight joint relevance metric for feature relevancy term. Engineering Applications of Artificial Intelligence. 106. 104481–104481. 24 indexed citations
16.
Gao, Wanfu, Juncheng Hu, Yonghao Li, & Ping Zhang. (2020). Feature Redundancy Based on Interaction Information for Multi-Label Feature Selection. IEEE Access. 8. 146050–146064. 14 indexed citations
17.
Hu, Juncheng, Yonghao Li, Wanfu Gao, & Ping Zhang. (2020). Robust multi-label feature selection with dual-graph regularization. Knowledge-Based Systems. 203. 106126–106126. 62 indexed citations
18.
Hu, Liang, Yonghao Li, Wanfu Gao, Ping Zhang, & Juncheng Hu. (2020). Multi-label feature selection with shared common mode. Pattern Recognition. 104. 107344–107344. 66 indexed citations
19.
Wang, Shibo, Julong Wei, Ruidong Li, et al.. (2019). Identification of optimal prediction models using multi-omic data for selecting hybrid rice. Heredity. 123(3). 395–406. 45 indexed citations
20.
Lin, Ying, Yi Zhu, Chuan Chen, et al.. (2017). Facing the challenges in ophthalmology clerkship teaching: Is flipped classroom the answer?. PLoS ONE. 12(4). e0174829–e0174829. 81 indexed citations

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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