Xinjun Peng

1.8k total citations · 1 hit paper
46 papers, 1.5k citations indexed

About

Xinjun Peng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Xinjun Peng has authored 46 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 22 papers in Control and Systems Engineering. Recurrent topics in Xinjun Peng's work include Face and Expression Recognition (36 papers), Advanced Algorithms and Applications (20 papers) and Machine Learning and ELM (8 papers). Xinjun Peng is often cited by papers focused on Face and Expression Recognition (36 papers), Advanced Algorithms and Applications (20 papers) and Machine Learning and ELM (8 papers). Xinjun Peng collaborates with scholars based in China. Xinjun Peng's co-authors include Dong Xu, De Chen, Jindong Shen, Yifei Wang, Wen Zhou, Yifei Wang, Yifei Wang, Yifei Wang and Xiang Liu and has published in prestigious journals such as The Journal of Chemical Physics, Expert Systems with Applications and Pattern Recognition.

In The Last Decade

Xinjun Peng

46 papers receiving 1.4k citations

Hit Papers

TSVR: An efficient Twin Support Vector Machine for regres... 2009 2026 2014 2020 2009 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. TSVR: An efficient Twin Support Vector Machine for regression
    2009 372 citations Xinjun Peng Neural Networks profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xinjun Peng China 20 960 856 434 153 129 46 1.5k
Yitian Xu China 25 1.3k 1.4× 1.4k 1.6× 382 0.9× 276 1.8× 175 1.4× 122 2.0k
Takashi Onoda Japan 12 476 0.5× 811 0.9× 252 0.6× 70 0.5× 39 0.3× 73 1.4k
Xijiong Xie China 19 1.1k 1.1× 825 1.0× 126 0.3× 118 0.8× 28 0.2× 50 1.6k
Weida Zhou China 14 412 0.4× 341 0.4× 128 0.3× 170 1.1× 25 0.2× 41 814
Lingfeng Niu China 16 447 0.5× 711 0.8× 71 0.2× 155 1.0× 32 0.2× 82 1.2k
M. Markou United Kingdom 9 259 0.3× 1.2k 1.4× 372 0.9× 26 0.2× 58 0.4× 16 1.7k
Chen‐Chia Chuang Taiwan 14 194 0.2× 639 0.7× 384 0.9× 31 0.2× 47 0.4× 63 1.1k
Guangchun Luo China 21 624 0.7× 767 0.9× 79 0.2× 24 0.2× 32 0.2× 95 2.1k
Deli Zhao China 18 708 0.7× 798 0.9× 84 0.2× 42 0.3× 24 0.2× 57 1.5k

Countries citing papers authored by Xinjun Peng

Since Specialization
Citations

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

Fields of papers citing papers by Xinjun Peng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xinjun Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Xinjun Peng. A scholar is included among the top collaborators of Xinjun Peng 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 Xinjun Peng. Xinjun Peng 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.
Peng, Xinjun, Dong Xu, & De Chen. (2020). Progressive transduction nonnegative matrix factorization for dimensionality reduction. Neurocomputing. 414. 76–89. 3 indexed citations
2.
Peng, Xinjun, Dong Xu, & De Chen. (2020). Robust distribution-based nonnegative matrix factorizations for dimensionality reduction. Information Sciences. 552. 244–260. 14 indexed citations
3.
Peng, Xinjun, et al.. (2017). A twin-hyperspheres support vector machine with automatic variable weights for data classification. Information Sciences. 417. 216–235. 12 indexed citations
4.
Peng, Xinjun, et al.. (2016). L 1 -norm loss based twin support vector machine for data recognition. Information Sciences. 340-341. 86–103. 34 indexed citations
5.
Peng, Xinjun, et al.. (2014). Improvements on twin parametric-margin support vector machine. Neurocomputing. 151. 857–863. 16 indexed citations
6.
Peng, Xinjun, Dong Xu, & Jindong Shen. (2014). A twin projection support vector machine for data regression. Neurocomputing. 138. 131–141. 30 indexed citations
7.
Peng, Xinjun & Dong Xu. (2013). A local information-based feature-selection algorithm for data regression. Pattern Recognition. 46(9). 2519–2530. 21 indexed citations
8.
Peng, Xinjun, et al.. (2013). Structural twin parametric-margin support vector machine for binary classification. Knowledge-Based Systems. 49. 63–72. 28 indexed citations
9.
Peng, Xinjun & Dong Xu. (2013). Local scaling heuristic-based regularization for pattern classification. Neurocomputing. 119. 264–272. 1 indexed citations
10.
Peng, Xinjun & Dong Xu. (2012). Robust minimum class variance twin support vector machine classifier. Neural Computing and Applications. 22(5). 999–1011. 18 indexed citations
11.
Peng, Xinjun & Dong Xu. (2012). Bi-density twin support vector machines for pattern recognition. Neurocomputing. 99. 134–143. 19 indexed citations
12.
Peng, Xinjun. (2011). Building sparse twin support vector machine classifiers in primal space. Information Sciences. 181(18). 3967–3980. 52 indexed citations
13.
Peng, Xinjun. (2010). Primal twin support vector regression and its sparse approximation. Neurocomputing. 73(16-18). 2846–2858. 55 indexed citations
14.
Peng, Xinjun, et al.. (2010). The robust and efficient adaptive normal direction support vector regression. Expert Systems with Applications. 38(4). 2998–3008. 6 indexed citations
15.
Peng, Xinjun. (2010). Least squares twin support vector hypersphere (LS-TSVH) for pattern recognition. Expert Systems with Applications. 37(12). 8371–8378. 25 indexed citations
16.
Peng, Xinjun. (2009). TSVR: An efficient Twin Support Vector Machine for regression. Neural Networks. 23(3). 365–372. 372 indexed citations breakdown →
17.
Peng, Xinjun & Yifei Wang. (2009). Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression. Chinese Journal of Chemical Physics. 22(5). 502–510. 1 indexed citations
18.
Zhou, Wen, et al.. (2008). Accelerated stochastic simulation algorithm for coupled chemical reactions with delays. Computational Biology and Chemistry. 32(4). 240–242. 5 indexed citations
19.
Zhou, Wen, et al.. (2008). “Final all possible steps” approach for accelerating stochastic simulation of coupled chemical reactions. Applied Mathematics and Mechanics. 29(3). 379–387. 2 indexed citations
20.
Peng, Xinjun & Yifei Wang. (2007). L-leap: accelerating the stochastic simulation of chemically reacting systems. Applied Mathematics and Mechanics. 28(10). 1361–1371. 5 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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