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  • 开启程序人生 创造美好未来
  • 开启程序人生 创造美好未来
  • 开启程序人生 创造美好未来
  • 开启程序人生 创造美好未来
  • 开启程序人生 创造美好未来

彭勇 副教授

硕士生导师

计科&软工

机器学习、模式识别与脑机交互

yongpeng@hdu.edu.cn
杭州电子科技大学1教南515
0571-86878578
  • 彭勇,男,安徽巢湖人,2015年6月博士毕业于上海交通大学计算机科学与工程系并加入杭州电子科技大学;2016年起任副研究员(副教授)、硕士生导师。主要从事机器学习、模式是识别与脑机交互相关研究。

  • 论文发表:
    [1] Yong Peng, Leijie Zhang, Wanzeng Kong, Feiwei Qin, Jianhai Zhang. Low rank spectral regression via matrix factorization for efficient subspace learning. Journal of Intelligent & Fuzzy Systems, DOI: 10.3233/JIFS-191752, 2020. (SCI,影响因子1.851)

    [2] Yong Peng, Yikai Zhang, Feiwei Qin, Wanzeng Kong. Joint non-negative and fuzzy coding with graph regularization for efficient data clustering. Egyptian Informatics Journal, doi: /10.1016/j.eij.2020.05.001, 2020. (SCI,影响因子3.119)

    [3] Yong Peng, Leijie Zhang, Wanzeng Kong, Feiwei Qin, Jianhai Zhang. Joint low-rank representation and spectral regression for robust subspace learning. Knowledge-Based Systems, 195, 105723, 2020. (SCI,影响因子5.921)

    [4] Yong Peng, Qingxi Li, Wanzeng Kong, Jianhai Zhang, Bao-Liang Lu, Andrzej Cichocki. Joint semi-supervised feature auto-weighting and classification model for EEG-based cross-subject sleep quality evaluation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, May 4-8, pages 946-950, 2020. (CCF推荐B类会议)

    [5] Yong Peng, Leijie Zhang, Wanzeng Kong, Feiping Nie, Andrzej Cichocki. Joint structured graph learning and unsupervised feature selection. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, pages 3572-3576, 2019. (CCF推荐B类会议)

    [6] Yong Peng, Yanfang Long, Feiwei Qin, Wanzeng Kong, Feiping Nie, Andrzej Cichocki. Flexible non-negative matrix factorization with adaptively learned graph regularization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, pages 3107-3111, 2019. (CCF推荐B类会议)

    [7] Yong Peng, Rixin Tang, Wanzeng Kong, Jianhai Zhang, Feiping Nie, Andrzej Cichocki. Joint graph learning and clustering via concept factorization. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, pages 3162-3166, 2019. (CCF推荐B类会议)

    [8] Yong Peng, Wanzeng Kong, Feiwei Qin, Feiping Nie. Manifold adaptive kernelized low-rank representation for semi-supervised image classification. Complexity, Volume 2018 (2018), Article ID 2857594, 2018.(SCI,影响因子2.462)
    [9] Yong Peng, Rixin Tang, Wanzeng Kong, Feiwei Qin, Feiping Nie. Parallel vector field regularized non-negative matrix factorization for image representation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, April 15-20, pages 2216-2220, 2018.( CCF推荐B类会议)

    [10] Yong Peng, Wanzeng Kong, Bing Yang. Orthogonal extreme learning machine for image classification. Neurocomputing, 266: 458-464, 2017.(SCI二区,影响因子4.072,引用16次)
    [11] Yong Peng, Bao-Liang Lu. Discriminative extreme learning machine with supervised sparsity preserving for image classification. Neurocomputing, 261: 242-252, 2017.(SCI,影响因子4.438,引用59次)

    [12] Yong Peng, Bao-Liang Lu. Robust structured sparse representation via half-quadratic optimization for face recognition. Multimedia Tools and Applications,76(6): 8859-8880, 2017. (SCI,影响因子2.313,引用15次)

    [13] Yong Peng, Bao-Liang Lu. Discriminative manifold extreme learning machine and applications to image and EEG signal classification. Neurocomputing, 174:265--277, 2016.(SCI,影响因子4.438,引用30次)

    [14] Yong Peng, Wei-Long Zheng, Bao-Liang Lu. An unsupervised discriminative extreme learning machine and its applications to data clustering. Neurocomputing, 174: 250--264, 2016.(SCI,影响因子4.438,引用29次)

    [15] Yong Peng, Xianzhong Long, Bao-Liang Lu. Graph based semi-supervised learning via structure preserving low rank representation. Neural Processing Letters, 41(3): 389--406,2015. (SCI,影响因子2.891,引用8次)

    [16] Yong Peng, Bao-Liang Lu, Suhang Wang. Enhanced low rank representation via sparse manifold adaption for semi-supervised learning. Neural Networks, 65: 1--17, 2015.(SCI一区TOP,影响因子5.535,引用34次)

    [17] Yong Peng, Bao-Liang Lu. Hybrid learning clonal selection algorithm. Information Sciences, 296: 128--146, 2015.(SCI,影响因子5.524,引用28次)

    [18] Yong Peng, Suhang Wang, Xianzhong Long, Bao-Liang Lu. Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing,149: 340--353, 2015.(SCI,影响因子4.438,引用85次,曾入选ESI高被引论文)

    [19] Wei-Long Zheng, Jia-Yi Zhu, Yong Peng, Bao-Liang Lu. EEG-based emotion classification using deep belief networks. IEEE International Conference on Multimedia and Expo (ICME), Chengdu, China, July 14-18, pages 1--6, 2014. (CCF推荐B类会议,引用193次)

    [20] Yong Peng, Bao-Liang Lu. A hierarchical particle swarm optimizer with latin sampling based memetic algorithm for numerical optimization. Applied Soft Computing, 13(5): 2823--2836, 2013.(SCI,影响因子5.472,引用25次)

  • 2009年度中国科学院院长奖;
    2018年度中国电子学会技术发明三等奖(参与);
  • 科研项目:
    [1] 国家自然科学基金面上项目, 主题:脑电情感识别, 01/2020-12/2023, 70.8万,负责人
    [2] 浙江省属高校基本科研业务费,主题:脑电情感识别,09/2020-2022/08,20万,负责人
    [3] 中国博士后科学基金, 主题:低秩数据建模与分析,01/2018-12/2019, 8万, 负责人
    [4] 教育部重点实验室基金, 主题:驾驶疲劳检测,01/2020-06/2020,10万,负责人
    [5] 国家自然科学基金青年项目, 主题:低秩数据建模与分析(已结题),01/2017-12/2019,23.8万,负责人
    [6] 浙江省公益计划, 主题:驾驶疲劳检测(已结题),01/2017-12/2019,20万,负责人
    [7] CCF-腾讯犀牛鸟创意基金, 主题:低秩数据建模与分析(已结题), 10/2017-12/2018,3万,负责人
    [8] 国防科技重点实验室基金,主题:目标检测与识别(已结题), 04/2019-06/2020, 5万,负责人
    [9] 安徽大学基金,主题:图聚类分析(已结题),01/2018-12/2019,5万,负责人