Chun-Na Li

Associate Professor

OPTIMAL research group for machine learning,

Zhejiang University of Technology (ZJUT), China.


Correspondence:

Address:  Chun-Na Li

Zhijiang College, Zhejiang University of Technology, HangZhou 310014, China


Office: 2C406, QiongLi Building, Zhijiang Campus of ZJUT
Tel: +86-0575-81112568
Home: http://www.optimal-group.org/member/lcn/
Email: na1013na@163.com

Chun-Na Li received her Master's degree and Ph.D degree in Department of Mathematics from Harbin Institute of Technology, China, in 2009 and 2012, respectively. Currently, she is an Associate Professor at the Zhijiang College, Zhejiang University of Technology. Her research interests include optimization methods, machine learning and data mining.


Publications:

  • Li C N, Shao Y H, Yin W, Liu M Z. Robust and sparse linear discriminant analysis via alternating direction method of multipliers. . IEEE Transactions on Neural Networks and Learning Systems. 2019, in press.
  • Li C N, Shang M Q,Shao Y H, Xu Y, Liu M Z, Wang Z. Sparse L1-norm two dimensional linear discriminant analysis via the generalized elastic net regularization. . Neurocomputing. 2019, 337, 80-96.
  • Chen W J, Li C N, Shao Y H. Robust two-dimensional locality preserving projection with regularization. . Knowledge-Based Systems. 2019, 169, 53-66.
  • Bai L, Shao Y H, Wang Z, Li C N. Clustering by twin support vector machine and least square twin support vector classifier with uniform output coding. Knowledge-Based Systems. 2019, 163, 227-240.
  • Bai L, Wang Z, Shao Y H, Li C N. Reversible discriminant analysis. IEEE Access. 2018, 6: 72551-72562.
  • Wang Z, Shao Y H, Bai L, Li C N, Liu M Z, Deng N Y. Insensitive stochastic gradient twin support vector machine for large scale problems. Information Sciences. 2018, 462: 114–131.
  • Chen W J, Li C N, Shao Y H, Deng N Y. Robust L1-norm multi-weight vector projection support vector machine with efficient algorithm. Neurocomputing. 2018, 315: 345-361.
  • Shao Y H, Li C N, Liu M Z, Wang Z, Deng N Y. Sparse Lq-norm least squares support vector machine with feature selection. Pattern Recognition. 2018, 78: 167-181.
  • Sun X Q, Chen Y J, Shao Y H, Li C N, Wang C H. Robust nonparallel proximal support vector machine with Lp-norm regularization. Knowledge-Based Systems. 2018, 6: 20334-20347.
  • Liu M Z, Shao Y H, Wang Z, Li C N, Chen W J. Minimum deviation distribution machine for large scale regression. Knowledge-Based Systems. 2018, 146: 167-180.
  • 邵元海,杨凯丽,刘明增,王震, 李春娜,陈伟杰. 从支持向量机到非平行支持向量机. 运筹学学报. 2018, 22(2): 55-65.
  • Wang C , Liang M , Li C N. Adaptive fuzzy sliding mode observer for cylinder mass flow estimation in SI engines. IEEE Access. 2018, 6: 29558-29566.
  • Wang C, Li C N, Pei H X, Guo Y R, Shao Y H. Alternating direction method of multipliers for L1-and L2-norm best fitting hyperplane classifier. Procedia Computer Science. 2017, 108: 1292-1301.
  • Pei H X, Zheng Z R, Wang C, Li C N, Shao Y H. D-FCM: Density based fuzzy c-means clustering algorithm with application in medical image segmentation. Procedia Computer Science. 2017, 122: 407-414.
  • C. N. Li, Z.R. Zheng, M.Z. Liu, Y.H. Shao, W.J.Chen. Robust recursive absolute value inequalities discriminant analysis with sparseness. Neural Networks. 2017, 93:205-218.
  • C. N. Li, Chen W J, Y.H. Shao. Sparse Lp-norm principal component analysis with robustness (In Chinese). Acta Automatica Sinica, 2017, 43(1): 142-151.
  • Y.F. Ye, Y.H. Shao, N.Y. Deng, C. N. Li, X.Y. Hua. Robust Lp-norm least squares support vector regression with feature selection. Applied Mathematics and Computation, 2017, 305: 32-52.
  • Ye Y F, Ying C, Shao Y H, C. N. Li, Y. J. Chen. Robust and sparse lp-norm support vector regression. Journal of advanced computational intelligence and intelligent informatics, 2017, 21(6): 989-997
  • Ye Y F, Ying C, Shao Y H, C. N. Li, Y. J. Chen. Robust and sparse lp-norm support vector regression. Journal of advanced computational intelligence and intelligent informatics, 2017, 21(6): 989-997.
  • C. N. Li, Y. H. Shao, N. Y. Deng. Robust L1-norm nonparallel proximal support vector machine. Optimization, 2016, 65(1):169-183. (SCI)
  • W. J. Chen, Y. H. Shao,C. N. Li, N. Y. Deng. MLTSVM: A novel twin support vector machine to multi-label learning. Pattern Recogn, 2016, 52: 61-74. (SCI)
  • W. J. Chen,C. N. Li, Y. H. Shao,N. Y. Deng. Semi-supervised projection twin support vector machine via manifold regularization. (In Chinese) Pattern Recogn. & Artif. Intell, 2016, 28(2): 97-107.
  • Yuan-Hai Shao, Zhen Wang, Chun-Na Li, Nai-Yang Deng. Local sensitive proximal classifier with consistency for small sample size problem. In: Proceedings of the 15th IEEE International Conference on Data Mining Workshops (ICDM'15), 2015.(EI)
  • Ya-Fen Ye,Yuan-Hai Shao, Chun-Na Li. Wavelet Lp-norm support vector regression with feature selection, Journal of Advanced Computational Intelligence and Intelligent Informatics, 2015, 19(3): 407-416. (EI)
  • Ya-Fen Ye, Yue-Xiang Jiang,Yuan-Hai Shao, Chun-Na Li. Financial conditions index construction through weighted Lp-norm support vector regression, Journal of Advanced Computational Intelligence and Intelligent Informatics, 2015, 19(3): 397-406. (EI)
  • C. N. Li, Y. H. Shao, N. Y. Deng. Robust L1-norm two-dimensional linear discriminant analysis. Neural Networks, 2015, 65: 92-104. (SCI)
  • Y. H. Shao, W. J. Chen, Z. Wang, C. N. Li, N. Y. Deng. Weighted linear loss twin support vector machine for large-scale classification. Knowledge-Based Systems, 2015, 73: 276-288. (SCI)
  • Z.-M. Yang, Y.-R. Guo, C. N. Li, Y.-H. Shao. Local k-proximal plane clustering. Neural Computing and Applications, 2015, 26(1): 199-211. (SCI)
  • Y.-H. Shao, C. N. Li, Z. Wang, M.-Z. Liu, N.-Y. Deng. Proximal classifier via absolute value inequalities. In: Proceedings of the 14th IEEE International Conference on Data Mining Workshops (ICDM'14), , 2014, 74-79. (EI)
  • C. N. Li, Y. F. Huang, H. J. Wu, Y. H. Shao, Z. M. Yang. Multiple recursive projection twin support vector machine for multi-class classification. International Journal of Machine Learning and Cybernetics, 2014, DOI: 10.1007/s13042-014-0289-2. (EI)
  • C. N. Li. Goodearl-Menal pairs of linear transformations. Journal of Mathematical Research with Applications, 2014, 34(2): 161-167. The core of Chinese core journals by Chinese Science Citation Database (CSCD)
  • G. H. Tang, C. N. Li, Y. Zhou. Study of Morita contexts. Communications in Algebra, 2014, 42(4): 1668-1681. (SCI)
  • C. N. Li, Y. Q. Zhou. On p.p. structural matrix rings. Linear Algebra and Its Applications, 2012, 436(9): 3692-3700. (SCI)
  • C. N. Li, L. Wang, Y. Q. Zhou. On rings with the Goodearl-Menal condition. Communications in Algebra, 2012, 40(12): 4679-4692. (SCI)
  • C. N. Li, Y. Q. Zhou. On strongly *-clean rings. Journal of Algebra and Its Applications, 2011, 10(6): 1363-1370. (SCI)

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