[1] Liang Bai, Jiye Liang, Fuyuan Cao. Semi-supervised clustering with constraints of different types from multiple information sources. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(9): 3247-3258.

[2] Xinyan Liang, Qian Guo, Yuhua Qian, Weiping Ding, Qingfu Zhang. Evolutionary Deep Fusion Method and Its Application in Chemical Structure Recognition. IEEE Transactions on Evolutionary Computation, 2021, 25(5): 883-893.

[3] Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang. A unified sample selection framework for output noise filtering: An error-bound perspective. Journal of Machine Learning Research, 2021, 22(18): 1-66.

[4] Jiye Liang, Junbiao Cui, Jie Wang, Wei Wei. Graph-based semi-supervised learning via improving the quality of the graph dynamically. Machine Learning, 2021, 110: 1345-1388.

[5] Xinyao Guo, Chuangyin Dang, Jianqing Liang, Wei Wei, Jiye Liang. Metric learning with clustering-based constraints. International Journal of Machine Learning and Cybernetics, 2021, 12: 3597-3605.

[6] Jie Wang, Jianqing Liang, Junbiao Cui, Jiye Liang. Semi-supervised learning with mixed-order graph convolutional networks. Information Sciences, 2021, 573: 171-181.

[7] Kaixuan Yao, Feilong Cao, Yee Leung, Jiye Liang. Deep neural network compression through interpretability-based filter pruning. Pattern Recognition, 2021, 119: 108056.

[8] Xingwang Zhao, Jiye Liang, Jie Wang. A community detection algorithm based on graph compression for large-scale social networks. Information Sciences, 2021, 551: 358-372.

[9] Liqin Yu, Fuyuan Cao, Xiaozhi Gao, Jing Liu, Jiye Liang. k-Mnv-Rep: a k-type clustering algorithm for matrix-object data. Information Sciences, 2021, 542: 40-57.

[10] Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang. An outlier detection algorithm for categorical matrix-object data. Applied Soft Computing, 2021, 104: 107182.