杜亮（副教授）

研究领域：人工智能与大数据计算、数据挖掘与机器学习

电子邮箱：duliang@sxu.edu.cn

个人简介

2007年武汉大学获软件工程学士学位，2013年中国科学院软件研究所计算机科学国家重点实验室获工学博士学位。2010-2011年在惠普（中国）研究院从事实习研究；2013-2014年在淘宝（中国）软件有限公司从事计算广告方面的开发研究；2014-2015年在中国科学院软件研究所参与973项目“网络信息空间大数据计算理论”的研究。

主要从事大规模多视图学习，图信号处理等算法研究工作。先后发表论文70多篇，其中在IEEE TKDE,IEEE TNNLS, IEEE TCYB, ACM TKDD, Information Fusion, PR,软件学报,中国科学:信息科学等知名期刊和KDD,AAAI,IJCAI,ICDM,SDM,CIKM等国际会议上发表论文50余篇。

主持国家自然科学基金面上项目2项，青年基金1项目，省级项目多项，产学研合作课题多项。曾获“三晋英才”青年优秀人才，中国科学院院长奖、北京市优秀毕业生等奖励。

更多信息见：https://csliangdu.github.io/

发表论文：

[1]Zhaolong Ling, Enqi Xu, Peng Zhou, Liang Du, Kui Yu, Xindong Wu. Fair Feature Selection: A Causal Perspective. ACM Transactions on Knowledge Discovery from Data. 2024, Early Access.

[2]Peng Zhou, Liang Du, Zhaolong Ling, Xia Ji, Xuejun Li, Yi-Dong Shen. Partial Clustering Ensemble. IEEE Transactions on Knowledge and Data Engineering, 2023, Early Access.

[3]Peng Zhou, Bicheng Sun, Xinwang Liu, Liang Du, Xuejun Li. Active clustering ensemble with self-paced learning. IEEE Transactions on Neural Networks and Learning Systems, 2023, Early Access.

[4]梁云辉, 杜亮. 基于图滤波与自表示的无监督特征选择算法. 吉林大学学报(理学版), 2023, Early Access.

[5]Liang Du, Yunhui Liang, Mian Ilyas Ahmad, Peng Zhou. K-Means Clustering with Chebyshev Polynomial Graph Filtering. IEEE International Conference on Acoustics, Speech, and Signal Processing, 7175-7179, 2024.

[6]Liang Du, Xiaodong Li, Yan Chen, Gui Yang, Mian Ilyas Ahmad, Peng Zhou. Higher Order Multiple Graph Filtering for Structured Graph Learning. IEEE International Conference on Acoustics, Speech, and Signal Processing, 7095-7099, 2024.

[7]Yan Chen, Liang Du, Peng Zhou, Lei Duan, Yuhua Qian. Multiple kernel clustering with local kernel reconstruction and global heat diffusion. Information Fusion, 105:102219, 2024.

[8]Peng Zhou, Boao Hu, Dengcheng Yan, Liang Du. Clustering Ensemble via Diffusion on Adaptive Multiplex. IEEE Transactions on Knowledge and Data Engineering, 36(4):1463-1474, 2024.

[9]Boao Hu, Xu Wang, Peng Zhou, Liang Du. Multi-view Outlier Detection via Graphs Denoising. Information Fusion, 101:102012, 2024.

[10]Peng Zhou, Liang Du, Xuejun Li. Adaptive Consensus Clustering for Multiple K-means via Base Results Refining. IEEE Transactions on Knowledge and Data Engineering, 35(10):10251-10264, 2023.

[11]Peng Zhou, Liang Du. Learnable Graph Filter for Multi-view Clustering. ACM International Conference on Multimedia, 3089-3098, 2023.

[12]Peng Zhou, Xia Wang, Liang Du. Bi-level ensemble method for unsupervised feature selection. Information Fusion, 100:101910, 2023.

[13]Peng Zhou, Jiangyong Chen, Liang Du, Xuejun Li. Balanced Spectral Feature Selection. IEEE Transactions on Cybernetics, 53(7):4232-4244, 2023.

[14]Peng Zhou, Xinwang Liu, Liang Du, Xuejun Li. Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering. ACM Transactions on Knowledge Discovery from Data, 17(5):1-35, 2023.

[15]梁云辉, 甘舰文, 陈艳, 周芃, 杜亮. 基于对偶流形重排序的无监督特征选择算法. 计算机科学, 50(7):72-81, 2023.

[16]王雷, 杜亮, 周芃. 基于稀疏连接的层次化多核K-Means算法, 计算机科学, 50(2):138-145, 2023.

[17]甘舰文, 陈艳, 周芃, 杜亮. 基于高阶一致性学习的聚类集成. 计算机应用, (9):2665 -2672, 2023.

[18]Jianwen Gan, Yunhui Liang, Liang Du. Local-Sample-Weighted Clustering Ensemble with High-Order Graph Diffusion. Mathematics, 11(6):1340, 2023.

[19]Bicheng Sun, Peng Zhou, Liang Du, Xuejun Li. Active Deep Image Clustering. Knowledge-Based Systems, 252:109346, 2022.

[20]Peng Zhou, Xia Wang, Liang Du, Xuejun Li. Clustering Ensemble via Structured Hypergraph Learning. Information Fusion, 78:171-179, 2022.

[21]Yan Chen, Lei Wang, Liang Du, Lei Duan. A Trace Ratio Maximization Method for Parameter Free Multiple Kernel Clustering. International Conference on Database Systems for Advanced Applications, 681-688, 2022.

[22]王雷, 杜亮, 周芃, 吴鹏. 基于自步学习的对称非负矩阵分解算法. 郑州大学学报(理学版), 43-48, 2022.

[23]Xiaoqin Zhang, Mingyu Fan, Di Wang, Peng Zhou, Dacheng Tao. Top-k Feature Selection Framework Using Robust 0-1 Integer Programming. IEEE Transactions on Neural Networks and Learning Systems,32(7):3005-3019, 2021.

[24]Peng Zhou, Liang Du, Yi-Dong Shen, Xuejun Li. Tri-level robust clustering ensemble with multiple graph learning. The AAAI Conference on Artificial Intelligence, 35(12):11125-11133, 2021.

[25]Peng Zhou, Liang Du, Xuejun Li. Self-paced consensus clustering with bipartite graph. The International Joint Conferences on Artificial Intelligence, 2133-2139, 2021.

[26]Xiaolin Lv, Liang Du. Graph-based Kullback-Leibler Divergence Minimization for Unsupervised Feature Selection. The International Conference on Machine Learning and Soft Computing, 109-114, 2021.

[27]吕晓林, 杜亮, 周芃, 吴鹏. 基于邻域区间扰动融合的无监督特征选择算法框架. 南京理工大学学报, 45(4):420-428, 2021.

[28]杜亮, 任鑫, 张海莹, 周芃. 基于局部回归融合的多核聚类方法. 计算机科学, 48(8):47-52, 2021.

[29]Peng Zhou, Yi-Dong Shen, Liang Du, Fan Ye. Self-paced clustering ensemble. IEEE Transactions on Neural Networks and Learning Systems, 32(4):1497-1511, 2020.

[30]Liang Du, Xin Ren, Peng Zhou, Zhiguo Hu. Unsupervised Dual Learning for Feature and Instance Selection. IEEE Access, 8:170248-170260, 2020.

[31]Peng Zhou, Jiangyong Chen, Mingyu Fan, Liang Du, Yi-Dong Shen, Xuejun Li. Unsupervised feature selection for balanced clustering. Knowledge-Based Systems, 193:105417, 2020.

[32]Feiyu Zhao, Liang Du. Robust Dual Concept Factorization for Data Clustering. International Conference on Artificial Intelligence and Advanced Manufacture, 28-32, 2020.

[33]Liang Du, Haiying Zhang, Xin Ren, Xiaolin Lv. Manifold Adaptive Multiple Kernel K-Means for Clustering. International Conference on Algorithms, Computing and Artificial Intelligence, 1-7, 2020.

[34]Lin Mu, Haiying Zhang, Liang Du, Jie Gui, Aidan Li, Xi Zhang. Discriminative Multiple Kernel Concept Factorization for Data Representation. IEEE Access, 8:175086 - 175100, 2020.

[35]Hua Zhao, Liang Du, Jianglai Wei, Yalong Fan. Local Sensitive Dual Concept Factorization for Unsupervised Feature Selection. IEEE Access, 8:133128-133143, 2020.

[36]Peng Zhou, Liang Du, Xuejun Li, Yi-Dong Shen, Yuhua Qian. Unsupervised Feature Selection with Adaptive Multiple Graph Learning. Pattern Recognition, 105:107375, 2020.

[37]Liang Du, Chaohong Ren, Xiaolin Lv, Yan Chen, Peng Zhou, Zhiguo Hu. Local graph reconstruction for parameter free unsupervised feature selection. IEEE Access, 7:102921-102930, 2019.

[38]Liang Du, Xiaolin Lv, Chaohong Ren, Yan Chen. A filter-based unsupervised feature selection method via improved local structure preserving. International Conference on Big Data and Information Analytics, 162-169, 2019.

[39]Peng Zhou, Yi-Dong Shen, Liang Du, Fan Ye. Incremental multi-view support vector machine. SIAM International Conference on Data Mining, 1-9, 2019.

[40]Peng Zhou, Yi-Dong Shen, Liang Du, Fan Ye, Xuejun Li. Incremental multi-view spectral clustering. Knowledge-Based Systems, 174:73-86, 2019.

[41]Peng Zhou, Fan Ye, Liang Du. Unsupervised robust multiple kernel learning via extracting local and global noises. IEEE Access, 7:34451-34461, 2019.

[42]Liang Du, Xiaolin Lv. Consensus graph weighting via trace ratio criterion for multi-view unsupervised feature selection. The International Conference on Data Mining Workshops, 615-619. 2019.

[43]李飞, 杜亮, 任超宏. 基于全局融合的多核概念分解算法. 计算机应用, 39(4):1021-1026,  2019.

[44]Yan Wu, Liang Du, Honghong Cheng. Multi-view k-means clustering with Bregman divergences. The International Conference on Artificial Intelligence, 26-38. 2018.

[45]Peng Zhou, Fan Ye, Liang Du. Spectral clustering with distinction and consensus learning on multiple views data. Plos one, 13(12):e0208494, 2018.

[46]Mingyu Fan, Xiaoqin Zhang, Liang Du, Liang Chen, Dacheng Tao. Semi-supervised learning through label propagation on geodesics. IEEE Transactions on Cybernetics, 48(5):1486-1499, 2017.

[47]Mingyu Fan, Xiaojun Chang, Xiaoqin Zhang, Di Wang, Liang Du. Top-k Supervise Feature Selection via ADMM for Integer Programming. The International Joint Conferences on Artificial Intelligence, 1646-1653, 2017.

[48]胡治国, 田春岐, 杜亮, 关晓蔷, 曹峰. IP 网络性能测量研究现状和进展. 软件学报, 28(1):105-134, 2016.

[49]Hanmo Wang, Liang Du, Peng Zhou, Lei Shi, Yuhua Qian, Yi-Dong Shen. Experimental design with multiple kernels. The IEEE International Conference on Data Mining, 419-428, 2015.

[50]Nannan Gu, Mingyu Fan, Liang Du, Dongchun Ren. Efficient sequential feature selection based on adaptive eigenspace model. Neurocomputing, 161: 199-209, 2015.

[51]Nannan Gu, Mingyu Fan, Di Wang, Lihao Jia, Liang Du. Semi-supervised classification based on affine subspace sparse representation. SCIENTIA SINICA Informationis, 45(8):985-1000, 2015.

[52]Liang Du, Yi-Dong Shen. Unsupervised Feature Selection with Adaptive Structure Learning. The ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 209-218, 2015.

[53]Hanmo Wang, Liang Du, Peng Zhou, Lei Shi, Yi-Dong Shen. Convex Batch Mode Active Sampling via alpha-relative Pearson Divergence. The AAAI Conference on Artificial Intelligence, 29(1):3045-3051, 2015.

[54]Peng Zhou, Liang Du, Mingyu Fan, Yi-Dong Shen. An LLE based Heterogeneous MetricLearning for Cross-media Retrieval. SIAM International Conference on Data Mining, 64-72, 2015.

[55]Liang Du, Peng Zhou, Lei Shi, Hanmo Wang, Mingyu Fan, Wenjian Wang, Yi-Dong Shen. Robust Multiple Kernel K-means using L21-norm. The International Joint Conferences on Artificial Intelligence, 3476-3482, 2015.

[56]Peng Zhou, Liang Du, Lei Shi, Hanmo Wang, Yi-Dong Shen. Recovery of Corrupted Multiple Kernels for Clustering. The International Joint Conferences on Artificial Intelligence, 4105-4111, 2015.

[57]Peng Zhou, Liang Du, Lei Shi, Hanmo Wang, Yi-Dong Shen. Learning a Robust Consensus Matrix for Clustering Ensemble via Kullback-Leibler Divergence Minimization. The International Joint Conferences on Artificial Intelligence, 4112-4118, 2015.

[58]Lei Shi, Liang Du, Yi-Dong Shen. Robust Spectral Learning for Unsupervised Feature Selection. The IEEE International Conference on Data Mining, 977-982, 2014.

[59]Liang Wu, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Jianhui Li, Yuanchun Zhou,  Xiong Hui. Heterogeneous metric learning with content-based regularization for software artifact retrieval. The IEEE International Conference on Data Mining, 610-619, 2014.

[60]Liang Wu, Alvin Chin, Guandong Xu, Liang Du, Xia Wang, Kangjian Meng, Yonggang Guo, Yuanchun Zhou. Who Will Follow Your Shop? Exploiting Multiple Information Sources in Finding Followers. The International Conference on Database Systems for Advanced Applications, 401-415, 2013.

[61]Liang Du, Zhiyong Shen, Xuan Li, Peng Zhou, Yi-Dong Shen. Local and Global Discriminative Learning for Unsupervised Feature Selection. The IEEE International Conference on Data Mining, 131-140, 2013.

[62]Deng, Jun, Liang Du, Yi-Dong Shen. Heterogeneous metric learning for cross-modal multimedia retrieval. The International Conference on Web Information Systems Engineering, 43-56, 2013.

[63]Liang Du, Yi-Dong Shen, Zhiyong Shen, Jianying Wang, Zhiwu Xu. A self-supervised framework for clustering ensemble. The International Conference on Web-Age Information Management, 253-264, 2013.

[64]Liang Du, Yi-Dong Shen. Joint clustering and feature selection. The International Conference on Web-Age Information Management, 241-252, 2013.

[65]Liang Du, Yi-Dong Shen. Towards robust co-clustering. The International Joint Conference on Artificial Intelligence, 1317-1322, 2013.

[66]Xuan Li, Liang Du, Yi-Dong Shen. Update Summarization via Graph-Based Sentence Ranking. IEEE Transactions on Knowledge and Data Engineering, 25(5):1162-1174, 2013.

[67]Liang Du, Xuan Li, Yi-Dong Shen. Robust Nonnegative Matrix Factorization Via Half-Quadratic Minimization. The IEEE International Conference on Data Mining, 201-210, 2012.

[68]Xuan Li, Liang Du, Yi-Dong Shen. Graph-Based Marginal Ranking for Update Summarization. The SIAM International Conference on Data Mining, 486-497, 2011.

[69]Liang Du, Xuan Li, Yi-Dong Shen. Cluster Ensembles via Weighted Graph Regularized Nonnegative Matrix Factorization. In Advanced Data Mining and Applications, 215-228, 2011.

[70]Liang Du, Xuan Li, Yi-Dong Shen. User Graph Regularized Pairwise Matrix Factorization for Item Recommendation. In Advanced Data Mining and Applications, 372-385, 2011.

[71]Xuan Li, Yi-Dong Shen, Liang Du, Chen-Yan Xiong. Exploiting novelty, coverage and balance for topic-focused multi-document summarization. The ACM international conference on Information and knowledge management, 1765-1768, 2010.

[72]Zhiyong Shen, Liang Du, Xukun Shen, Yi-Dong Shen. Interval-valued Matrix Factorization with Applications. The IEEE International Conference on Data Mining, 1037-1042, 2010.