代表性成果 |
[1] Mei Lu, Xiangjun Zhao, Li Zhang, Fanzhang Li, Semi-supervised concept factorization for document clustering, Information Sciences, 2016, 331(2): 86~98. [2] Mei Lu, Li Zhang, Xiangjun Zhao, Fanzhang Li. Constrained neighborhood preserving concept factorization for data representation. KNOWLEDGE-BASED SYSTEMS, 2016. 102: 127-139. [3] Mei Lu, Li Zhang, Fanzhang Li. Adaptively local consistent concept factorization for multi-view clustering. Soft Computing. 26. 1-13. 10.1007/s00500-021-06526-2 (2022). [4] Mei Lu, Fanzhang Li. Survey on lie group machine learning. Big Data Mining and Analytics, vol. 3, no. 4, pp. 235-258, Dec. 2020.2020.9020011. [6] 路梅; 李凡长, 邻域嵌入的张量学习, 计算机科学与探索, 2017, 2017(7): 1102~1113. [7] 路梅; 李凡长. 张量树学习算法 , 南京大学学报(自然科学版), 2015.3.30,(02): 390~404. [8] Jiaohong Yi, Mei Lu, Xiangjun Zhao. Quantum inspired monarch butterfly optimization for UCAV path planning navigation problem. Int. J. Bio Inspired Comput. 15(2): 75-89 (2020). [9] Gaige Wang, Mei Lu, Xiangjun Zhao. An improved bat algorithm with variable neighborhood search for global optimization. CEC 2016: 1773-1778. [10] Gaige Wang, Mei Lu, Yongquan Dong, Xiangjun Zhao. Self-adaptive extreme learning machine[J]. Neural Computing and Applications, 2016, 27(2): 291-303. [11] Yanhong Feng, Juan Yang, Congcong Wu, Mei Lu, Xiangjun Zhao. Solving 0-1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation. Memetic Comput. 10(2): 135-150 (2018) [12] Yanhong Feng, GaiGe Wang, Suash Deb, Mei Lu, Xiangjun Zhao. Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization[J]. Neural computing and applications, 2017, 28(7): 1619-1634. |