基本信息
| 姓名：杨晓伟 办公室：B7-218 E-mail: xwyang@scut.edu.cn 所在团队：数据科学与软件工程 个人主页： |
个人简介
杨晓伟，男，博士，教授，博士生导师。分别于1991年、1996年和2000年在吉林大学数学系、数学所和工程力学系获得理论与应用力学专业学士、计算力学专业硕士和固体力学专业博士学位。研究领域为：机器学习和模式识别。主要学术贡献包括：（一）在支持向量机领域，提出了基于核模糊C-均值聚类及最远距离策略的模糊支持向量机分类算法和基于非凸优化的鲁棒最小二乘支持向量机算法，首次证明了在高斯核所引导的高维特征空间中，由模糊C-均值聚类算法生成的类中心未必在原始低维特征空间中存在原像，纠正了高维特征空间中样本点到类中心的距离计算公式；（二）在张量分析领域，首次提出了线性支持高阶张量机分类模型，克服了早期支持张量机分类模型没有闭式解的不足，从理论上说明了标准支持向量机分类模型是该模型的一个特例；（三）在迁移学习领域，首次从统计学的角度出发提出了对齐源域和目标域联合分布的浅层和深层迁移学习算法，解决了迁移学习的一个本质性问题。相关成果主要发表在IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Cybernetics、IEEE Transactions on Evolutionary Computation、IEEE Transactions on Fuzzy Systems、IEEE Transactions on Image Processing、IEEE Transactions on Software Engineering、IEEE Transactions on Knowledge and Data Engineering、IEEE Transactions on Geoscience and Remote Sensing、IEEE Transactions on Medical Imaging、Pattern Recognition、Neurocomputing、ICSE、ICDM、SDM等国内外人工智能、模式识别和软件工程主流杂志和国际学术会议上。另外，基于在支持向量机和张量学习方面的多年研究基础，在科学出版社出版了学术专著《支持向量机的算法设计与分析》和《张量学习理论及其应用》，其中《支持向量机的算法设计与分析》纳入到了“信息与计算科学丛书”中；《张量学习理论及其应用》纳入到“统计与数据科学丛书”中。到目前为止，指导学术型硕士和博士80余人，其中3人获得国家级青年人才项目资助，5人获得广东省青年人才项目资助。
学历
1987.09--1991.07 吉林大学数学系理论与应用力学专业学习，获理学学士学位
1993.09--1996.07 吉林大学数学所计算力学专业学习，获理学硕士学位
1997.03--2000.06 在吉林大学工程力学系固体力学专业学习，工学博士
教学经历
机器学习
计算智能
算法设计与分析
离散数学
数值分析
常微分方程
高等数学
工作经历
1991.07--1993.09 国营新乡振动设备总厂工作
1996.07--2001.06 吉林大学数学系 讲师
2001.06--2015.01 华南理工大学数学学院 讲师、副教授、教授、博士生导师
2003.01--2003.06 新加坡国立大学机械工程学院 访问学者
2006.02--2006.08 澳大利亚悉尼科技大学信息技术学院 访问学者
2009.07--2010.07 澳大利亚悉尼科技大学软件学院 访问教授
2015.01-- 华南理工大学软件学院，教授、博士生导师
2015.06--2022.10 华南理工大学软件学院 副院长
社会兼职
广州工业与应用数学学会副理事长
中国人工智能学会机器学习专业委员会委员
中国中文信息学会社会媒体处理专业委员会委员
研究方向
机器学习与模式识别：特别专注于支持向量机、领域自适应、张量学习理论及其应用的研究
获奖情况
2005 广东省自然科学二等奖
2007 广东省自然科学三等奖
2009 第六届高等教育国家级教学成果奖二等奖
2009 第一届华南理工大学“我最喜爱的导师”提名奖
2010 全国商业科技进步奖一等奖
2012全国商业科技进步奖二等奖
2013 广东省自然科学二等奖
2015 广东省科技进步一等奖
2017 第八届华南理工大学“我最喜爱的导师”奖
科研项目
承担了2020年度科技部“新一代人工智能”重大项目、2018-2019年度广东省“新一代人工智能”重大项目、国家自然科学基金面上项目、国家社科基金重大项目、广东省科技攻关项目、广东省自然科学基金、广州市科技计划基础研究类项目和华为等大型企业委托项目10余项。
发表文章
1.Pei Hang, Zhaoming Kong, Limin Wang, Xueming Han, and Xiaowei Yang, Efficient and Stable Unsupervised Feature Selection Based on Novel Structured Graph and Data Discrepancy Learning, IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(4): 6229-6243.
2.Mengying, Xie, Xiaolan Liu, Xiaowei Yang, A non-local self-similarity based weighted tensor low-rank decomposition for multi-channel image completion with mixture noise, IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(1): 73-87.
3.Pei Huang, Mengying Xie, Xiaowei Yang, Unsupervised feature selection via controllable adaptive graph learning and discriminative feature learning, IEEE Transactions on Neural Networks and Learning Systems, 2024, 35 (11): 15600-15614.
4.向毅，黄翰，罗川，杨晓伟，基于多样性SAT求解器和新颖性搜索的软件产品线测试，软件学报，2024，35（6）：2821-2843。
5.Yi Xiang, Han Huang, Sizhe Li, Minqing Li, Chuan Luo, and Xiaowei Yang, Automated Test Suite Generation for Software Product Lines Based on Quality-Diversity Optimization, ACM Transactions on Software Engineering and Methodology, 2023, 33(2):1-46.
6.Mengying Xie, Xiaolan Liu, Xiaowei Yang, Wenzeng Cai, Multi-channel image completion with mixture noise: adaptive sparse low-rank tensor subspace meets non-local self-similarity, IEEE Transactions on Cybernetics, 2023, 53(12): 7521-7534.
7.Sentao Chen, Zijie Hong, Mehrtash Harandi, Xiaowei Yang, Domain neural adaptation, IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(11): 8630-8641.
8.Yingying Chen, Zijie Hong, Xiaowei Yang, Cost-sensitive online adaptive kernel learning for large-scale imbalanced classification, IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 10554-10568.
9.Yi Xiang, Xiaowei Yang, Han Huang, Jiahai Wang, Balancing constraints and objectives by considering problem types in constrained multi-objective optimization, IEEE Transactions on Cybernetics, 2023, 53(1): 88-101.
10.Sentao Chen, Lei Wang, Zijie Hong, Xiaowei Yang, Domain generalization by joint-product distribution alignment, Pattern Recognition, 2023, 134: 109086.
11.Mengying Xie, Xiaolan Liu, Zijie Hong, Xiaowei Yang, Novel hybrid low-rank tensor approximation for hyperspectral image mixed denoising based on global-guided-nonlocal prior mechanism, IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022, 60: 5541817.
12.Yi Xiang, Han Huang, Miqing Li, Sizhe Li, Xiaowei Yang, Looking for novelty in search-based software product line testing, IEEE Transactions on Software Engineering, 2022, 48(7): 2317-2338.
13.Yi Xiang, Han Huang, Yuren Zhou, Sizhe Li, Chuan Luo, Qingwei Lin, Miqing Li, Xiaowei Yang, Search-based diverse sampling from real-world software product lines, ICSE, 2022, 1945-1957.
14.Yingying Chen, Xiaowei Yang, Online adaptive kernel learning with random features for large-scale nonlinear classification, Pattern Recognition, 2022, 131: 108862.
15.Pei Huang, Xiaowei Yang, Unsupervised feature selection via adaptive graph and dependency score, Pattern Recognition, 2022, 127: 108622.
16.Xiang, Yi; Yang, Xiaowei, Huang, Han; Huang, Zhengxin; Li, Miqing, Sampling configurations from software product lines via probability-aware diversification and SAT solving, Automated Software Engineering, 2022, 29(2): 54.
17.Sentao Chen, Mehrtash Harandi, Xiaona Jin, Xiaowei Yang, Semi-supervised domain adaptation via asymmetric joint distribution matching, IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(12): 5708-5722.
18.Liangqi Cai, Wen Wen, Biao Wu, Xiaowei Yang, A coarse-to-fine user preferences prediction method for point-of-interest recommendation, Neurocomputing, 2021, 422: 1-11.
19.Sentao Chen, Le Han, Xiaolan Liu, Zongyao He, Xiaowei Yang, Subspace distribution adaptation frameworks for domain adaptation, IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(12): 5204-5218.
20.Sentao Chen, Mehrtash Harandi, Xiaona Jin, Xiaowei Yang, Domain adaptation by joint distribution invariant projections, IEEE Transactions on Image Processing, 2020, 29: 8264-8277.
21.Yi Xiang, Xiaowei Yang*, Yuren Zhou, Han Huang, Enhancing decomposition-based algorithms by estimation of distribution for constrained optimal software product selection, IEEE Transactions on Evolutionary Computation, 2020, 24(2): 245-259.
22.Yi Xiang, Yuren Zhou*, Xiaowei Yang, Han Huang, A many-objective evolutionary algorithm with Pareto-adaptive reference points，IEEE Transactions on Evolutionary Computation, 2020, 24(1): 99-113.
23.Yi Xiang, Xiaowei Yang, Yuren Zhou, Zibin Zheng, Miqing Li and Han Huang, Going deeper with optimal software products selection using many-objective optimization and satisfiability solvers, Empirical Software Engineering, 2020, 25(1), 591-626.
24.Xiaona Jin, Xiaowei Yang, Bo Fu, Sentao Chen, Joint distribution matching embedding for unsupervised domain adaptation, Neurocomputing, 2020, 412: 115-128.
25.Zisen Fang, Xiaowei Yang, Le Han, Xiaolan Liu, A sequentially truncated higher singular value decomposition based algorithm for tensor completion, IEEE Transactions on Cybernetics, 2019, 49(5): 1956-1967.
26.Zhaoming Kong, Xiaowei Yang, Color image and multispectral image denoising using block diagonal representation, IEEE Transactions on Image Processing, 2019, 28(9): 4247-4259.
27.Han Huang, Yihui Liang, Xiaowei Yang, Zhifeng Hao, Pixel-level discrete multiobjective sampling for image matting, IEEE Transactions on Image Processing, 2019, 28(8): 3739-3751.
28.张宇山，黄翰，郝志峰，杨晓伟, 连续型演化算法首达时间分析的平均增益模型, 计算机学报, 2019, (03): 624-635。
29.Zhaoming Kong, Le Han, Xiaolan Liu, Xiaowei Yang, A new 4-D nonlocal transform-domain filter for 3-D magnetic resonance images denoising, IEEE Transactions on Medical Imaging, 2018, 37(4): 941-954.
30.Le Han, Zhen Wu, Kui Zeng, Xiaowei Yang, Online multilinear principal component analysis, Neurocomputing, 2018, 275：888-896.
31.Xiaolan Liu, Tengjiao Guo, Lifang He, Xiaowei Yang, A low-rank decomposition based transductive support tensor machine for semi-supervised classification, IEEE Transactions on Image Processing, 2015, 24(6):1825-1838.
32.Xiaowei Yang, Liangjun Tan, Lifang He, A robust least squares support vector machine for regression and classification with noise, Neurocomputing, 2014, 140, pp. 41-52.
33.Tengjiao Guo, Le Han, Lifang He, Xiaowei Yang, A GA-based feature selection and parameter optimization for linear support higher-order tensor machine, Neurocomputing, 2014. 144, pp. 408-416.
34.Lifang He, Xiangnan Kong, Philip S. Yu, Ann B. Ragin, Zhifeng Hao, Xiaowei Yang, DuSK: A dual structure-preserving kernel for supervised tensor learning with applications to neuroimages, SDM, 2014, 127-135.
35.Zhifeng Hao, Lifang He, Bingqian Chen, and Xiaowei Yang, A linear support higher-order tensor machine for classification, IEEE Transactions on Image Processing, 2013, 22(7): 2911-2920.
36.Xiaowei Yang, Qiaozhen Yu, Lifang He, Tengjiao Guo, The one-against-all partition based binary tree support vector machine algorithms for multi-class classification, Neurocomputing, 2013, 113: 1-7.
37.Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma, A kernel fuzzy c-means clustering based fuzzy support vector machine algorithm for classification problems with outliers or noises, IEEE Transactions on Fuzzy Systems, 2011, 19(1): 105-115.
38.Xiaowei Yang, Jie Lu, Guangquan Zhang, Adaptive pruning algorithm for least squares support vector machine classifier, Soft Computing, 2010, 14(7), 667-680.
39.Ruichu Cai, Zhifeng Hao, Xiaowei Yang, Wen Wen, An efficient gene selection algorithm based on mutual information, Neurocomputing, 2009, 72(4-6): 991-999.
40.Wen Wen, Zhifeng Hao, Xiaowei Yang, A heuristic weight-setting strategy and iteratively updating algorithm for weighted least-squares support vector regression, Neurocomputing, 2008, 71(16-18): 3096-3103.
