World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
9124
World Ranking
8268
National Ranking
1081

Guoxu Zhou publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Guoxu Zhou sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 138 publications — 22nd percentile

22% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Guoxu Zhou D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Guoxu Zhou sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Guoxu Zhou is affiliated with Guangdong University of Technology in China and has contributed extensively to fields related to computer science and engineering. Their research primarily covers areas including computer vision and pattern recognition, computational mathematics, computational mechanics, artificial intelligence, and radiology, nuclear medicine, and imaging.

Their research topics are centered around tensor decomposition and its applications, sparse and compressive sensing techniques, advanced neuroimaging techniques and applications, advanced image and video retrieval techniques, face and expression recognition, domain adaptation and few-shot learning, and blind source separation techniques.

Among Guoxu Zhou's recent papers are:

  • Noisy Tensor Completion via Low-Rank Tensor Ring (2022), published in IEEE Transactions on Neural Networks and Learning Systems
  • Low Tensor-Ring Rank Completion by Parallel Matrix Factorization (2020), published in IEEE Transactions on Neural Networks and Learning Systems
  • Improving EEG Decoding via Clustering-Based Multitask Feature Learning (2021), published in IEEE Transactions on Neural Networks and Learning Systems
  • Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation (2022), published in IEEE/CAA Journal of Automatica Sinica
  • Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement (2020), published in IEEE Transactions on Neural Systems and Rehabilitation Engineering

Their frequent co-authors include Qibin Zhao, Shengli Xie, Yuning Qiu, Zhenhao Huang, and Weijun Sun.

Guoxu Zhou has published widely in venues such as arXiv (Cornell University), Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Science China Technological Sciences, and IEEE Signal Processing Letters.

Best Publications

  • Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

    Andrzej Cichocki;Danilo Mandic;Lieven De Lathauwer;Guoxu Zhou

  • Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis

    A. Cichocki;D. Mandic;A-H. Phan;C. Caiafa

  • Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis.

    Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang

  • Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

    Yu Zhang;Chang S. Nam;Guoxu Zhou;Jing Jin

  • L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

    Yu Zhang;Guoxu Zhou;Jing Jin;Minjue Wang

  • Sparse Bayesian Classification of EEG for Brain–Computer Interface

    Yu Zhang;Guoxu Zhou;Jing Jin;Qibin Zhao

  • Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

    Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang

  • Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces

    Yu Zhang;Yu Wang;Guoxu Zhou;Jing Jin

  • Bayesian Robust Tensor Factorization for Incomplete Multiway Data

    Qibin Zhao;Guoxu Zhou;Liqing Zhang;Andrzej Cichocki

  • Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction

    Guoxu Zhou;Andrzej Cichocki;Yu Zhang;Danilo P. Mandic

  • Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

    Zhaoshui He;Shengli Xie;R. Zdunek;Guoxu Zhou

  • Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data

    Guoxu Zhou;Qibin Zhao;Yu Zhang;Tulay Adali

  • Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization

    Zuyuan Yang;Guoxu Zhou;Shengli Xie;Shuxue Ding

  • Multiway canonical correlation analysis for frequency components recognition in SSVEP-Based BCIs

    Yu Zhang;Guoxu Zhou;Qibin Zhao;Akinari Onishi

  • EEG classification using sparse Bayesian extreme learning machine for brain–computer interface

    Zhichao Jin;Guoxu Zhou;Daqi Gao;Yu Zhang

  • Fast Nonnegative Matrix/Tensor Factorization Based on Low-Rank Approximation

    Guoxu Zhou;Andrzej Cichocki;Shengli Xie

  • Time-Frequency Approach to Underdetermined Blind Source Separation

    Shengli Xie;Liu Yang;Jun-Mei Yang;Guoxu Zhou

  • Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface

    Yu Zhang;Guoxu Zhou;Qibin Zhao;Jing Jin

  • Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective

    Guoxu Zhou;Andrzej Cichocki;Qibin Zhao;Shengli Xie

  • Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI

    Haiqiang Wang;Yu Zhang;Nicholas R. Waytowich;Dean J. Krusienski

Frequent Co-Authors

Andrzej Cichocki
Andrzej Cichocki Systems Research Institute
Shengli Xie
Shengli Xie Guangdong University of Technology
Xingyu Wang
Xingyu Wang East China University of Science and Technology
Jing Jin
Jing Jin East China University of Science and Technology
Liqing Zhang
Liqing Zhang Shanghai Jiao Tong University
Tulay Adali
Tulay Adali University of Maryland, Baltimore County
Danilo P. Mandic
Danilo P. Mandic Imperial College London
Asoke K. Nandi
Asoke K. Nandi Brunel University London
Shun-ichi Amari
Shun-ichi Amari RIKEN Center for Brain Science

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