Shao-Bo Lin

1.9k total citations
59 papers, 1.1k citations indexed

About

Shao-Bo Lin is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shao-Bo Lin has authored 59 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Artificial Intelligence, 27 papers in Computational Mechanics and 26 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shao-Bo Lin's work include Sparse and Compressive Sensing Techniques (23 papers), Neural Networks and Applications (21 papers) and Image and Signal Denoising Methods (12 papers). Shao-Bo Lin is often cited by papers focused on Sparse and Compressive Sensing Techniques (23 papers), Neural Networks and Applications (21 papers) and Image and Signal Denoising Methods (12 papers). Shao-Bo Lin collaborates with scholars based in China, Hong Kong and United States. Shao-Bo Lin's co-authors include Ding‐Xuan Zhou, Zongben Xu, Jian Fang, Jinshan Zeng, Xia Liu, Zongben Xu, Zheng-Chu Guo, Xiangyu Chang, Feilong Cao and Hangbin Wu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing.

In The Last Decade

Shao-Bo Lin

58 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shao-Bo Lin China 17 657 379 245 142 88 59 1.1k
Purushottam Kar India 14 647 1.0× 311 0.8× 145 0.6× 51 0.4× 45 0.5× 32 1.0k
Manuel Davy France 22 810 1.2× 252 0.7× 67 0.3× 112 0.8× 125 1.4× 57 1.5k
Li-e Wang China 11 239 0.4× 201 0.5× 440 1.8× 296 2.1× 38 0.4× 50 1.2k
Zhanjie Song China 20 175 0.3× 688 1.8× 118 0.5× 48 0.3× 29 0.3× 88 1.2k
Wan Luo China 6 399 0.6× 247 0.7× 237 1.0× 97 0.7× 18 0.2× 16 1.3k
Yoshinobu Kawahara Japan 19 338 0.5× 171 0.5× 131 0.5× 74 0.5× 81 0.9× 85 1.1k
E. Alper Yıldırım Türkiye 13 193 0.3× 195 0.5× 133 0.5× 73 0.5× 35 0.4× 43 925
J. Vermaak United Kingdom 17 867 1.3× 649 1.7× 112 0.5× 446 3.1× 28 0.3× 37 1.7k
Yanning Shen United States 17 427 0.6× 171 0.5× 374 1.5× 246 1.7× 16 0.2× 74 1.2k

Countries citing papers authored by Shao-Bo Lin

Since Specialization
Citations

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

Fields of papers citing papers by Shao-Bo Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shao-Bo Lin. 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 Shao-Bo Lin. The network helps show where Shao-Bo Lin may publish in the future.

Co-authorship network of co-authors of Shao-Bo Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Shao-Bo Lin. A scholar is included among the top collaborators of Shao-Bo Lin 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 Shao-Bo Lin. Shao-Bo Lin 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.
Wang, Di, et al.. (2024). Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data. SIAM Journal on Imaging Sciences. 17(2). 951–983. 1 indexed citations
2.
Zeng, Jinshan, Shao-Bo Lin, Yuan Yao, & Ding‐Xuan Zhou. (2021). On ADMM in Deep Learning: Convergence and Saturation-Avoidance. Journal of Machine Learning Research. 22(199). 1–67. 5 indexed citations
3.
Lin, Shao-Bo, Yu Guang Wang, & Ding‐Xuan Zhou. (2021). Distributed Filtered Hyperinterpolation for Noisy Data on the Sphere. SIAM Journal on Numerical Analysis. 59(2). 634–659. 10 indexed citations
4.
Lin, Shao-Bo, Jian Fang, & Xiangyu Chang. (2020). Learning With Selected Features. IEEE Transactions on Cybernetics. 52(4). 2032–2046. 3 indexed citations
5.
Lin, Shao-Bo, Di Wang, & Ding‐Xuan Zhou. (2020). Distributed Kernel Ridge Regression with Communications. Journal of Machine Learning Research. 21(93). 1–38. 2 indexed citations
6.
Lin, Shao-Bo, Yunwen Lei, & Ding‐Xuan Zhou. (2019). Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping. Journal of Machine Learning Research. 20(46). 1–36. 11 indexed citations
7.
Fang, Jian, Shao-Bo Lin, & Zongben Xu. (2018). Learning Through Deterministic Assignment of Hidden Parameters. IEEE Transactions on Cybernetics. 50(5). 2321–2334. 8 indexed citations
8.
Lin, Shao-Bo. (2018). Nonparametric regression using needlet kernels for spherical data. Journal of Complexity. 50. 66–83. 5 indexed citations
9.
Zeng, Jinshan, et al.. (2018). Block Coordinate Descent for Deep Learning: Unified Convergence Guarantees.. arXiv (Cornell University). 1 indexed citations
10.
Lin, Shao-Bo, Xin Guo, & Ding‐Xuan Zhou. (2017). Distributed Learning with Regularized Least Squares. Journal of Machine Learning Research. 18(92). 1–31. 42 indexed citations
11.
Chang, Xiangyu, Shao-Bo Lin, & Ding‐Xuan Zhou. (2017). Distributed semi-supervised learning with kernel ridge regression. Journal of Machine Learning Research. 18(1). 1493–1514. 40 indexed citations
12.
Zeng, Jinshan, et al.. (2017). GAITA: A Gauss–Seidel iterative thresholding algorithm for ℓq regularized least squares regression. Journal of Computational and Applied Mathematics. 319. 220–235. 5 indexed citations
13.
Guo, Zheng-Chu, Shao-Bo Lin, & Lei Shi. (2017). Distributed learning with multi-penalty regularization. Applied and Computational Harmonic Analysis. 46(3). 478–499. 20 indexed citations
14.
Lin, Shao-Bo. (2016). Linear and nonlinear approximation of spherical radial basis function networks. Journal of Complexity. 35. 86–101. 2 indexed citations
15.
Fang, Jian, Shao-Bo Lin, & Zongben Xu. (2015). Learning and approximation capabilities of orthogonal super greedy algorithm. Knowledge-Based Systems. 95. 86–98. 10 indexed citations
16.
Chen, Xu, Shao-Bo Lin, Jian Fang, & Runze Li. (2015). Prediction-based Termination Rule for Greedy Learning with Massive Data. Statistica Sinica. 26(2). 841–860. 3 indexed citations
17.
Lin, Shao-Bo, Jinshan Zeng, Lin Xu, & Zongben Xu. (2014). Jackson-type inequalities for spherical neural networks with doubling weights. Neural Networks. 63. 57–65. 4 indexed citations
18.
Lin, Shao-Bo, Feilong Cao, Xiangyu Chang, & Zongben Xu. (2012). A general radial quasi-interpolation operator on the sphere. Journal of Approximation Theory. 164(10). 1402–1414. 5 indexed citations
19.
Lin, Shao-Bo, Feilong Cao, & Zongben Xu. (2011). Essential rate for approximation by spherical neural networks. Neural Networks. 24(7). 752–758. 13 indexed citations
20.
Cao, Feilong, Xiaofei Guo, & Shao-Bo Lin. (2011). LpError Estimates for Scattered Data Interpolation On Spheres. Numerical Functional Analysis and Optimization. 32(12). 1205–1218. 3 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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