高维概率与统计研究：高维统计理论、高维数据分析算法及应用。

        机器学习理论及算法研究：非负矩阵分解、数据融合、数据预测理论及算法。

        在大数据时代，数据维度呈爆炸式增长，传统统计方法难以应对。高维概率统计理论和算法能有效处理高维数据，揭示数据内在规律，为科学研究和实际应用提供重要思想和关键技术支持。机器学习理论及算法研究是推动人工智能发展的核心动力。非负矩阵分解、数据融合和预测算法等，能提升机器学习模型的性能和泛化能力，使计算机更好地理解和处理复杂数据，在众多领域发挥关键作用，推动各行业的智能化转型，对社会发展具有深远影响。

       深入探究高维统计理论，包括高维变量间的复杂依赖关系建模、高维假设检验方法等，为分析海量数据提供理论支撑；同时致力于开发高效的高维数据分析算法，如高维聚类、降维算法，挖掘数据潜在结构。非负矩阵分解通过将数据分解为非负基矩阵和系数矩阵，挖掘数据的局部特征，在图像识别、文本挖掘等领域表现卓越。数据融合技术整合多源异构数据，提升数据质量与信息丰富度，为决策提供更全面依据。数据预测理论及算法则致力于构建精准预测模型，涵盖时间序列预测、回归预测等。

      杨有龙教授负责的“高维数据分析团队”是学校“三好三有”导学团队，以研究高维概率统计理论为基础，开展相应的高维数据分析算法及应用研究，欢迎感兴趣的同学加入本科研团队！

已发表的论文：

[92]Xiaowan Ren, Youlong Yang. Adaptive Hypergraph Structure Regularized Semi-supervised Non-negative Matrix Factorization for Image Clustering. Neurocomputing. June 29, 2025, Accepted. 

[91]Haixia Shi, Youlong Yang. Consistent Learning for Incomplete Multi-View Clustering.  Engineering Applications of Artificial Intelligence. June 28, 2025, Accepted.

[90]Xiaowan Ren, Youlong Yang. Semi-supervised Symmetric Non-negative Matrix Factorization With Graph Quality Improvement And Constraints. Applied Intelligence. 2025,55(397):1-22. 

[89]Wenyue Huang, Jingjian Zhang, Youlong Yang. A storage-efficient LSH scheme for high-dimensional ANN search. Pattern Recognition. 2025,168, 111805:1-13. 

[88]Keyu Ma, Youlong Yang, Zeping Ge. Two-factor Smoothed Incomplete Multi-view Clustering via Inconsistent Guidance. Neurocomputing.  May 3，2025, Accepted. 

[87]Zhangqian Mu,Yuanyuan Liu,Youlong Yang. A large-scale group decision making model with a clustering algorithm based on a locality sensitive hash function，Engineering Applications of Artificial Intelligence. 2025, 140,109697:1-16.

[86]An Huang,Youlong Yang,Yuanyuan Liu. A Binary Risk Linguistic Fuzzy Behavioral TOPSIS Model for Multi-attribute Large-Scale Group Decision-Making Based on Risk Preference Classification and Adaptive Weight Updating. International Journal of Fuzzy Systems.2024,26(6):1852–1878.

[85]Zeping Ge,Youlong Yang. A simple multi-constraint fusion based semi-supervised non-negative matrix decomposition for image clustering，Neurocomputing. 2024,609:1-12. 128432.

[84]Zhenye Du, Youlong Yang, Tong Ning and Kaitian Gao.Proving the Security of Mediated Semi-Quantum Key Distribution Using Entropic Uncertainty Relation. Advanced Quantum Technologies. 2024,7(10):1-13.2400190.

[83]Zeping Ge,Youlong Yang, Zhenye Du.Integrated Self-supervised Label Propagation For Label Imbalanced Sets.  Applied Intelligence.2024,54: 8525–8544. 

[82]Linlin Zhao, Youlong Yang, Tong Ning. A Three-stage multimodal emotion recognition network based on text low-rank fusion.Multimedia Systems. 2024,30(3):1-16.

[81]Yongchun Wang,Youlong Yang, Tong Ning. Local structure learning for incomplete multi-view clustering. Applied Intelligence.Applied Intelligence. 2024,54: 3308–3324

[80]Zhenye Du, Youlong Yang, Tong Ning. Mediated semi-quantum key distribution protocol mixing single-state and entangled-state. EPL. 2024,145(2):1-7.28001.

[79]Jingjian Zhang, Youlong Yang, Yuanyuan Liu.Locality Sensitive Hashing for Approximate NearestNeighbor Search Based on Online Learning. Journal of Visual Communication And Image Representation. 2024,98,104036:1-11.

[78]Yixia Wang, Youlong Yang. Image Fusion Approach Based on Heterogeneous Dense Network, 2023 International Conference on Cyber-physical Social Intelligence, 2023ICCSI 38-43. DOI：10.1109/ICCSI58851.2023.10303896

[77]Jiadi Zhu, Youlong Yang. Imputation for single-cell RNA-seq data with nonnegative matrix factorization and transfer learning.  Journal of Bioinformatics and Computational Biology. 2023,21(6),2350029. 

[76]Haiquan Qiu,Youlong Yang,Hua Pan. Underestimation modification for intrinsic dimension estimation. Pattern Recognition. 2023,140,109580:1-12.

[75]Tong Ning, Youlong Yang, Zhenye Du. Quantum algorithm for twin extreme learning machine. Physica Scripta. 2023,98(8):1-12.

[74]Zhenye Du, Youlong Yang, Tong Ning. Security analysis for single state circular mediated semi-quantum key distribution. Quantum Information Processing. 2023,22(7), 280: 1-18.

[73]Tong Ning, Youlong Yang. Quantum algorithm for the covariance matrix preparation and its application. EPL. 2023,143(1),18001:1-7 .

[72]Yuanyuan Liu,Youlong Yang. Liqin Sun. Managing multi-granular probabilistic linguistic information in large-scale group decision making: A personalized individual semanticsbased consensus model. Expert systems with application. 2023,230,120645:1-31. 

[71]Jiadi Zhu, Youlong Yang. scMEB: A fast and clustering-independent method for detecting differentially expressed genes in single-cell RNA-seq data. BMC Genomics. 2023,24(1),280:1-15 .

[70]Jiaxuan Zhang,Youlong Yang.Density-Distance Outlier Detection Algorithm Based on Natural Neighborhood. Axioms. 2023,12(5):1-17 .

[69]Liqin Sun,Youlong Yang,Yuanyuan Liu,Tong Ning. Feature selection based on a hybrid simplified particle swarm optimization algorithm with maximum separation and minimum redundancy.International Journal of Machine Learning and Cybernetics. 2023,14(3):789-816 .

[68]Ding Zhang,Youlong Yang,Two-stage semi-supervised clustering ensemble framework based on constraint weight. International Journal of Machine Learning and Cybernetics. 2023, 14(2):567-586.

[67]Liqin Sun,Youlong Yang,Tong Ning. A novel feature selection using Markov blanket representative set and Particle Swarm Optimization algorithm.Computational & Applied Mathematics,2023.42(81):1-32.

[66]Tong Ning, Youlong Yang, Zhenye Du. Quantum kernel logistic regression based Newton method. Physica A: Statistical Mechanics and its Applications. 2023, 611,128454:1-11. 

[65]Jinxing Che,Fang Yuan,Suling Zhu,Youlong Yang.An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load.Applied Energy.2022,328,120156:1-14.

[64]Yuanyuan Liu, Youlong Yang, A probabilistic linguistic opinion dynamics method based on the DeGroot model for emergency decision-making in response to COVID-19,Computers & Industrial Engineering, 2022,173,108677:1-21. 

[63]Xuying Bai,Youlong Yang,  Fuzzy decision tree algorithm based on feature value’s class contribution level，Iranian Journal of Fuzzy Systems, 2022. 19(4):73-88. 

[62]Yuanyuan Liu, Youlong Yang, An extended VIKOR method based on particle swarm optimization and novel operations of probabilistic linguistic term sets for multicriteria group decision-making problem，International Journal of Intelligent Systems, 2022, 37(8):5381-5424.

[61]Mingxue Jiang, Youlong Yang, Haiquan Qiu, Fuzzy entropy and fuzzy support-based boosting random forests for imbalanced data，Applied Intelligence, 2022,52(4):4126–4143.

[60]Yuanyuan Liu, Youlong Yang, A novel similarity-based consensus model for probabilistic linguistic sets and its application in multi-attribute large-scale group decision making, Computational and Applied Mathematics, 2022,41,97:1-35.

[59]Liqin Sun, Youlong Yang, Tong Ning, Jiadi Zhu, A novel grey power-Markov model for the prediction of China's electricity consumption，Environmental Science and Pollution Research, 2022,29: 21717–21738.

[58]Zhiwen Hua, Youlong Yang, Robust and sparse label propagation for graph-based semi-supervised classification，Applied Intelligence, 2022, 52(3): 3337–3351. 

[57]Haiquan Qiu, Youlong Yang, Saeid Rezakhah, Intrinsic dimension estimation method based on correlation dimension and kNN method，Knowledge-Based Systems, 2022, 10:235:1-17.

[56]Yuying Wang, Youlong Yang, Relative density-based clustering algorithm for identifying diverse density clusters effectively，Neural Computing & Applications, 2021,33(16): 10141-10157.

[55]Guoli Niu, Youlong Yang, Liqin Sun, One-step multi-view subspace clustering with incomplete views，Neurocomputing, 2021, 438:290-301.

[54]Haiquan Qiu, Youlong Yang, Benchong Li, Intrinsic dimension estimation based on local adjacency information，Information Sciences, 2021, 558(1): 21-33.

[53]Zhiwen Hua, Youlong Yang, Haiquan Qiu, Node influence-based label propagation algorithm for semi-supervised learning，Neural Computing & Applications, 2021, 33(7): 2753-2768.

[52]Yi Wang, Youlong Yang, Xi Zhao. Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images. European Conference on Computer Vision 2020 Workshops(ECCV 2020 Workshops).Lecture Notes in Computer Science, 2021,12538:651-664. 

[51]Danni Wei, Youlong Yang, Haiquan Qiu, Improving self-training with density peaks of data and cut edge weight statistic，Soft Computing, 2020, 24(20): 15595-15610.

[50]Yachong Li, Youlong Yang, Label Embedding for Multi-label Classification Via Dependence Maximization，Neural Processing Letters, 2020, 52(2): 1651-1674.

[49]Xu Wu, Youlong Yang, Lingyu Ren, Entropy difference and kernel-based oversampling technique for imbalanced data learning，Intelligent Data Analysis, 2020, 24(6): 1239-1255.  

[48]Jiaqi Ren, Youlong Yang, Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features，Neural Computing & Applications, 2020, 32(9): 4785-4804.

[47]Fang Yuan, Youlong Yang, Tiantian Yuan, A dissimilarity measure for mixed nominal and ordinal attribute data in k-Modes algorithm，Applied Intelligence, 2020, 50(5): 1498-1509.

[46]Ruonan Ren, Youlong Yang, Liqin Sun, Oversampling technique based on fuzzy representativeness difference for classifying imbalanced data，Applied Intelligence, 2020, 50(8): 2465-2487.

[45]Lingyu Ren, Youlong Yang, Liqin Sun, Xu Wu, Grey-based multiple instance learning with multiple bag-representative，AI Communications, 2020, 33(2): 59-73. https://content.iospress.com/articles/ai-communications/aic200628

[44]Nina Fei, Youlong Yang，Xuying Bai. One Core Task of Interpretability in Machine Learning - Expansion of Structural Equation Modeling. International Journal of Pattern Recognition and Artificial Intelligence, 2020. 34(1), p1-25.

[43]Youlong Yang, Mengxiao Ding, Decision function with probability feature weighting based on Bayesian network for multi-label classification，Neural Computing & Applications, 2019, 31(9): 4819-4828.

[42]Youlong Yang, Jinxing Che, Chengzhi Deng, Li, Li，Sequential grid approach based support vector regression for short-term electric load forecasting, Applied Energy, 2019, 238(6): 1010-1021.

[41]Yanying Li, Jinxing Che, Youlong Yang, Subsampled support vector regression ensemble for short term electric load forecasting，Energy, 2018, 164: 160-170.

[40]Mengxiao Ding, Youlong Yang, Zhiqing Lan, Multi-label imbalanced classification based on assessments of cost and value，Applied Intelligence, 2018, 48(10): 3577-3590.

[39]Gencheng Liu, Youlong Yang, Benchong Li, Fuzzy rule-based oversampling technique for imbalanced and incomplete data learning，Knowledge-Based Systems, 2018, 158: 154-174.

[38]Benchong Li, Youlong Yang, Complexity of concept classes induced by discrete Markov networks and Bayesian networks，Pattern Recognition, 2018, 82: 31-37. 

[37]JinXing Che and Youlong Yang，Stochastic correlation coefficient ensembles for variable selection. Journal of Applied Statistics. 2017. 44(10):1721-1742.

[36]Jinxing Che, Youlong Yang, Li Li; Xuying Bai, Shenghu Zhang, Chengzhi Deng，Maximum relevance minimum common redundancy feature selection for nonlinear data，Information Sciences, 2017, 409(10): 68-86.

[35]JinXing Che, Youlong Yang, Li Li; YanYing Li, SuLing Zhu，A modified support vector regression: Integrated selection of training subset and model, Applied Soft Computing, 2017, 53(4): 308-322.

[34]Nina Fei, Youlong Yang, Estimating linear causality in the presence of latent variables. Cluster Computing-The Journal of Networks, Software Tools and Applications. 2017. 20(2): 1025-1033.

[33]Dandan Yan, Youlong Yang, Benchong Li, An improved fuzzy classifier for imbalanced data，Journal of Intelligent & Fuzzy Systems, 2017, 32(3): 2315-2325.

[32]Youlong Yang , Dandan Yan, Junhang Zhao. Optimal path selection approach for fuzzy reliable shortest path problem. Journal of Intelligent & Fuzzy Systems. 2017,32(1),p197-205.

[31]Youlong Yang, Jinxing Che, Yanying Li, Yanjun Zhao, Suling Zhu. An incremental electric load forecasting model based on support vector regression. Energy. 2016. 113(15):796-808.

[30]Yanying Li,Youlong Yang,Wensheng Wang and Wenming Yang. An algorithm for learning the skeleton of large Bayesian network．International Journal on Artificial Intelligence Tools．2015, 24(4): 1-19.

[29]Youlong Yang, Yan Wu, VE dimension induced by Bayesian networks over the boolean domain, Pattern Analysis and Applications, 2014, 17(4): 799-807.

[28]Xia Liu, Youlong Yang, Mingmin Zhu, Structure-learning of causal Bayesian networks based on adjacent nodes, International Journal on Artificial Intelligence Tools, 2013, 22(2): 100-104.

[27]Mingmin Zhu, Sanyang Liu, Youlong Yang, Propagation in CLG Bayesian networks based on semantic modeling, Artificial Intelligence Review, 2012, 38(2): 149-162.

[26]Mingmin Zhu, Sanyang Liu, Youlong Yang, Kui Liu, Using junction trees for structural learning of Bayesian networks. Journal of Systems Engineering and Electronics, 2012, 23(2): 286-292. 

[25]Youlong Yang, Yan Wu, On the properties of concept classes induced by multivalued Bayesian networks. Information Sciences, 2012. 184(1): 155-165.

[24]Youlong Yang, Yan Wu, VC dimension and inner product space induced by Bayesian networks. International Journal of Approximate Reasoning, 2009, 50(7): 1036-1045.

[23]Youlong Yang, Yan Wu, Inner Product Space and Concept Classes Induced by Bayesian Networks. Acta Applicandae Mathematicae, 2009, 106(3): 337-348.

[22]Youlong Yang, Yan Wu, Sanyang Liu，Graphical model construction based on evolutionary algorithms. Journal of Control Theory and Applications, 2006, 4(4): 349-354.

[21]Youlong Yang, Yan Wu, Factorization of Width-two CSL Algebras and Nest Algebras. Chinese Quarterly Journal of Mathematics, 2006, 21(1): 10-14.

[20]任若楠,杨有龙,孙丽芹.基于模糊代表度的不平衡数据重采样方法.统计与决策. 2021,37(14):11-15.

[19]姜明雪,杨有龙.基于密度峰值聚类和模糊支持度的boosting随机森林.南京大学学报(自然科学版). 2021,57(4):582-590.

[18]刘莎, 杨有龙. 基于灰色关联分析的类中心缺失值填补方法.四川大学学报（自然科学版）.2020,57(5):871-788.

[17]李亚重,杨有龙,仇海全.一种基于嵌入式的弱标记分类算法.南京大学学报(自然科学版).2020,56(4):549-560.

[16]魏洒洒,杨有龙,赵伟卫.基于集成算法的SVM训练数据选择.统计与决策.2018,(9):77-80.

[15]魏正韬,杨有龙,白婧.基于非平衡数据的随机森林分类算法改进.重庆大学学报.2018,41(4):54-62.

[14]李艳颖,杨有龙,汪春峰.基于粗糙集属性约简与进化算法的贝叶斯网络分类器.郑州大学学报(理学版).2014,46(2):43-49.

[13]朱明敏,刘三阳,杨有龙.基于混合方式的贝叶斯网络等价类学习算法.电子学报.2013,41(1):98-104.

[12]朱明敏,刘三阳,杨有龙.基于最大主子图分解的贝叶斯网络等价类学习算法.控制与决策.2012,27(10):1499-1504.

[11]施轶青,杨有龙, 徐逸文.利用Boosting算法提升k依赖贝叶斯分类器分类性能.计算机科学.2010.37(7A):217-219.

[10]杨有龙,刘蔚,吴艳.贝叶斯网络的非忠实性分布.智能系统学报.2009,4(4):335-338.

[9]杨有龙,吴艳.基于进化算法的贝叶斯网络度量.兵工学报.2004,25(5):586-590.

[8]杨有龙,高晓光.套代数的直和算子集及可逆算子.数学研究与评论,2004,24(2):312-316.

[7]高晓光,杨有龙.基于不同威胁体的无人作战飞机初始路径规划.航空学报,2003,24(5):435-438.

[6]杨有龙,高晓光.紧致遗传算法的进化机制分析.控制理论与应用,2003,20(3):415-418.

[5]杨有龙,高晓光.匹配于进化种群的局部网络图度量.系统工程与电子技术.2003,25(6):734-737.

[4]杨有龙,高晓光.基于BD度量的局部网络结构分析.模式识别与人工智能,2003,16(1):17-21

[3]杨有龙,高晓光.关于二宽度CSL代数的Jacobson根.数学学报.2001.44(6):1107-1112.

[2]杨有龙,杜鸿科,张建华.二宽度CSL代数的直和分解.数学学报,1998,41(2):355-360.

[1]杜鸿科,杨有龙. 套代数的直和分解. 数学学报. 1995,38(6):782-788.