男，1979年11月出生，籍贯辽宁凌源。2002年7月毕业于吉林大学通信工程学院，获工学学士学位；2007年12月毕业于北京邮电大学信息工程学院, 获工学博士学位，同年留校执教，现为北京邮电大学人工智能学院副教授、硕士生导师、博士生导师。2011年1月入选微软亚洲研究院(MSRA)“铸星计划”(StarTrack)，2011年7月至2012年4月在MSRA视觉计算组(Visual Computing Group)访问研究；2012年12月至2013年11月底受国家留学基金委(CSC)青年骨干教师出国研修计划资助，在美国约翰·霍普金斯大学(The Johns Hopkins University)生物医学工程系成像科学研究中心视觉-动态-学习实验室访问研究; 2019年12月至2020年2月受北京邮电大学首批“双一流”建设引导专项资助，在美国约翰·霍普金斯大学(JHU)数据科学数学研究所(Johns Hopkins Mathematical Institute for Data Science)访问研究。研究方向为数据科学与机器学习，研究兴趣为高维数据建模、分析与学习及其在信号处理、模式识别、生物信息学以及精准医学中的应用，包括无监督注意力机制、稀疏/低秩模型(子空间聚类与结构化矩阵填充)、多元时间序列分析与基因表达分析等。目前已主持完成国家自然科学基金项目2项，累计在本领域国际学术会议和国际学术期刊上发表相关研究论文60余篇, 完成译著1部, 谷歌学术引用3100余次, 与所指导的研究生杨涛共同撰写的研究论文荣获2019年IEEE 视觉通信与图像处理大会(VCIP)最佳学生论文奖。现为CCF-CV专委会执行委员/CSIG-MV专委会委员/CCF-AI专委会执行委员，IEEE高级会员(Senior Member)/CCF/CSIG会员，曾担任2020年国际模式识别大会(ICPR2020)领域主席(Area Chair)和2021年IEEE机器视觉与模式识别大会(CVPR)领域主席(Area Chair)，担任40+国际国内学术期刊和学术会议审稿人。
[5] 学术报告：Self-Expressive Models for Clustering High Dimensional Data, at 人工智能论坛第71期, 腾讯会议(637 6868 7744), Oct. 23, 2024. [2024-10-21]
[4] 所指导的硕士生闻其帅的论文"Rethinking Decoders for Transformer-based Semantic Segmentation: Compression is All You Need"被NeurIPS2024接收！[PDF][code][poster][2024-09-26]
[3] 欢迎数学基础扎实的有志于做研究的考研同学提早与我联系！[2024-9-25]
[2] 历时2年半所完成翻译的《基于低维模型的高维数据分析: 原理、计算和应用》一书已由机械工业出版社出版发行(四色彩印,精装平价)！[出版社官网] [2024-8-31]
[1] 所指导的博士生薛超的研究论文“A Max-Flow based Approach for Neural Architecture Search”被欧洲计算机视觉大会ECCV2022接收！[2022-07-04]
[5] 2020年01月 至 现在 北京邮电大学 人工智能学院 人工智能与网络搜索教研中心 副教授 \ 硕导 (2016.06) \ 博导 (2019.07)
[4] 2015年12月 至 2019年12月 北京邮电大学 信息与通信工程学院 网络搜索教研中心 副教授 \ 硕导 (2016.06) \ 博导 (2019.07)
[3] 2008年01月 至 2015年12月 北京邮电大学 信息与通信工程学院 网络搜索教研中心 讲师
[2] 2002年9月 至 2007年12月 北京邮电大学 信息工程学院 信号与信息处理专业 获工学博士学位
[1] 1998年9月 至 2002年07月 吉林大学 南湖校区(原长春邮电学院) 通信工程专业 获学士学位
[4] 2019年07月获北京邮电大学首批“双一流”建设引导专项资助，于2019年12月至2020年2月 在美国约翰·霍普金斯大学(Johns Hopkins University)数据科学数学研究所(Mathematical Institute for Data Science)访问研究 (with Rene Vidal)
[3] 2016年07月受NSFC-RS国际合作交流项目资助，在英国伦敦玛丽女王大学(Queen Mary University of London)计算机视觉与多媒体(Computer Vision and Multimedia)组访问研究 (with Tao Xiang & Yi-Zhe Song)
[2] 2012年12月至2013年11月底受国家留学基金委(CSC)青年骨干教师出国研修计划资助，在美国约翰·霍普金斯大学(Johns Hopkins University)生物医学工程系(BME)视觉-动态-学习实验室(Vision, Dynamics, Learning Lab)访问研究 (with Rene Vidal)
[1] 2011年7月至2012年4月受微软亚洲研究院(MSRA)"铸星计划"(StarTrack)资助，在MSRA视觉计算(VC)研究组访问研究 (with Zhouchen Lin & Yi Ma )
研究方向: 数据科学与机器学习，研究兴趣为无监督/半监督/弱监督学习(特别是面向高维数据的建模与分析)及其在信号处理、模式识别、生物信息学以及精准医学中的应用，包括注意力模型、子空间聚类、多元时序数据分析和基因表达谱分析等。(*欢迎对上述研究方向感兴趣、真正热爱科研、做事踏实认真的同学随时与我联系，可合作研究或攻读学位) 近年来，主持完成国家自然科学基金项目2项，主持完成留学回国人员科研启动项目1项，参加完成国家自然科学基金面上项目4项，参与完成国家自然科学基金委与英国皇家学会合作交流项目1项，主持完成北京邮电大学科研创新专项3项，主持完成国际合作项目1项、横向委托研发项目1项。在IEEE TPAMI/J.STSP/TSP/TIP/TCSVT/TITS/TSMC/TNNLS/JBHI, PR, Neurocom., NeurIPS, ICCV, CVPR, ECCV, BMVC, ACCV, IJPRAI, ICDAR, ACM Multimedia, ICPR, ICASSP, ICIP, ACPR, VCIP等国际期刊和国际会议上发表论文60余篇, 完成译著1部, 所指导的研究生杨涛荣获2019年IEEE视觉通信与图像处理大会(VCIP)最佳学生论文奖, 曾担任国际模式识别大会(ICPR2020)领域主席(Area Chair)、IEEE模式识别与机器视觉大会(CVPR2021)领域主席。现为中国计算机协会(CCF)人工智能与模式识别专委会(CCF-AI)执行委员, 中国图像图形学会(CSIG)机器视觉专委会(CSIG-MV)委员, 国际电子电气工程师协会(IEEE)高级会员(Senior Member), 中国计算机协会(CCF)和中国图像图形学会(CSIG)会员。
[9] 知识增强的子空间聚类, 主持, 国家自然科学基金面上项目, 项目批准号: 61876022, 2019.01-2022.12. [已结题]
[8] 北京大学机器感知与智能教育部重点实验室开放课题系列, 课题编号:K-2019-03/K-2018-03/K-2017-04/K-2016-08, 2016.10-2019.12. [已结题]
[7] 基于结构化低秩准则的缺值填充问题研究, 主持, 教育部留学回国人员科研启动项目, 项目批准号: 留48, 2014.09-2016.12. [已结题]
[6] 基于激活力的复杂网络建模及其应用, 参与(排名第3位), 国家自然科学基金面上项目, 项目批准号: 61273217, 2013.01-2016.12. [已结题]
[5] 国家自然科学基金委与英国皇家学会合作交流项目, 参与, 项目批准号: 61511130081, 2015.04-2017.03. [已结题]
[4] 高维模式分析与学习, 主持, 北京邮电大学青年科研创新计划专项, 课题编号:2012R0108, 2012.01-2013.12. [已结题]
[3] 基于视觉认知的图像不变特征提取, 参与(排名第2位), 国家自然科学基金面上项目, 项目批准号: 61175011, 2012.01-2015.12. [已结题]
[2] 丛流形学习及其在物体识别中的应用, 主持, 国家自然科学基金委青年科学基金, 项目批准号:61005004, 2011.01-2011.12. [已结题]
[1] 基于多种物体识别的标签生成技术项目(MORE), 主持, 企业合作项目(编号I068-2008), 2008.11 至 2009.01. [已结题]
代表性论文 (* password will prompt when pointing the download link with your mouse)
[12] Chen Zhao, Chun-Guang Li, Wei He, and Chong You, "Deep Self-expressive Learning", Proc. of Machine Learning Research, Vol.234, pp.228-247, Conference on Parsimony and Learning (CPAL), Jan.3-6, 2024, Hongkong, P.R. China. [pdf][code]
[11] Mingkun Li, Chun-Guang Li, and Jun Guo, “Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification ”, IEEE Trans. Image Processing, Vol.31, May 16, 2022, pp.3606-3617.[arXiv][DOI:10.1109/TIP.2022.3173163][code]
[10] Shangzhi Zhang, Chong You, Rene Vidal and Chun-Guang Li, “Learning a Self-Expressive Network for Subspace Clustering ”, In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.[pdf][code][arXiv]
[9] Ying Chen, Chun-Guang Li, and Chong You, “Stochastic Sparse Subspace Clustering”, In Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.4155-4164.[pdf][arXiv][ code ]
[8] Chong You, Chun-Guang Li, Daniel P. Robinson, and Rene Vidal, “Is an Affine Constraint Needed for Affine Subspace Clustering?”, In Proc. of IEEE International Conference on Computer Vision(ICCV) 2019, pp.9915-9924, Oct. 27-Nov. 2, 2019, Seoul, Korea. [pdf][longer version(with proofs)][code]
[7] Junjian Zhang, Chun-Guang Li, Chong You, Xianbiao Qi, Honggang Zhang, Jun Guo, Zhouchen Lin, “Self-Supervised Convolutional Subspace Clustering Network”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5473-5482, Jun. 16-20, 2019, Long Beach, California, USA. [pdf][code]
[6] Chun-Guang Li, Chong You, and René Vidal, “On Geometric Analysis of Affine Sparse Subspace Clustering”, IEEE Journal of Selected Topics in Signal Processing, Vol.12, Issue 6, pp.1520-1533, Dec. 2018. DOI: 10.1109/JSTSP.2018.2867446 [pdf]
[5] Chun-Guang Li, Chong You, and René Vidal, “Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework”, IEEE Transactions on Image Processing, Vol. 26, No. 6, pp.2988-3001, June 2017. DOI:10.1109/TIP.2017.2691557 [pdf][code][notes]
[4] Chun-Guang Li and Rene Vidal. “A Structured Sparse plus Structured Low-Rank Framework for Subspace Clustering and Completion”, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp.6557-6570, Dec.15, 2016. [pdf][code][data][notes] DOI: 10.1109/TSP.2016.2613070
[3] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Lag Vegas, Nevada, US. [pdf][code](oral paper)
[2] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo, “Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning”, In Proc. of IEEE International Conference on Computer Vision (ICCV), Dec. 11-18, 2015, pp.2767-2775, Santiago, Chile. [pdf][spotlight][poster][code]
[1] Chun-Guang Li and René Vidal, “Structured Sparse Subspace Clustering: A Unified Optimization Framework”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.277-286, June 7-12, 2015, Boston, Massachusetts, US.[pdf][code][notes]
完整论文列表 (*鼠标停留在链接上,可提示密码)
I. 国际期刊:
2024
[25] Chao Xue, Jiaxing Li, Xiaoxing Wang, Yibing Zhan, Junchi Yan and Chun-Guang Li, “On Neural Architecture Search and Hyperparameter Optimization: A Max-Flow based Approach”, Submitted to Neural Networks, Sept. 25, 2024.
[24] Yuying Zhao, Mei Wang, Jiani Hu, Weihong Deng and Chun-Guang Li, “A Perturbed Match Filtering Approach for Face Image Quality Assessment”, Submitted to Pattern Recognition, July 6, 2024. [Major Revision]
[23] Wei He, Shangzhi Zhang, Chun-Guang Li, Xianbiao Qi, Rong Xiao, and Jun Guo,“Neural Normalized Cut: A Differential and Generalizable Approach for Spectral Clustering”, Submitted to Pattern Recognition, Jan. 29, 2024. [Major Revision]
[22] Qian Li, Chao Xue, Mingming Li, Chun-Guang Li, Chao Ma, and Xiaokang Yang, “Neural Architecture Selection as a Nash Equilibrium with Batch Entanglement”, IEEE Trans. Neural Networks and Learning Systems, Vol.35, No.11, pp.15195-15209, Nov., 2024. [pdf] DOI: 10.1109/TNNLS.2023.3283239
2022
[21] Mingkun Li, Chun-Guang Li, and Jun Guo, “Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification ”, IEEE Trans. Image Processing, Vol.31, May 16, 2022, pp.3606-3617.[arXiv][DOI:10.1109/TIP.2022.3173163][code]
[20] Mingkun Li, He Sun, Chaoqun Lin, Chun-Guang Li, and Jun Guo, “The Devil in the Tail: Clustering Consolidation plus Cluster Adaptive Balancing Loss for Unsupervised Person Re-Identification”, Pattern Recognition, Vol.129, Sept. 2022, 108763. [arXiv][DOI:10.1016/j.patcog.2022.108763]
[19] Bingcong Li, Xin Tang, Xianbiao Qi, Yihao Chen, Chun-Guang Li, Rong Xiao, “Effective Multi-Hot Encoding and Classifier for Lightweight Scene Text Recognition with a Large Character Set”, IEEE Trans. on Circuits and Systems for Video Technology, Vol.32, No.8, pp.5374-5385, August 2022. [PDF]
[18] Yihao Chen, Xin Tang, Xianbiao Qi, Chun-Guang Li, and Rong Xiao, “Learning Graph Normalization for Graph Neural Networks”, Neurocomputing, Vol.493, Jan. 2022, 613-625. [arXiv][code][DOI]
[17] Chao Xue, Mengting Hu, Xueqi Huang, and Chun-Guang Li, “Automated Search Space and Search Strategy Selection for Neural AutoML”, Pattern Recognition, Vol.124, 108474, April 2022.[pdf]
2021
[16] Shuai Di, Qi Feng, Chun-Guang Li, Mei Zhang, Honggang Zhang, Chiu C. Tan, and Haibin Ling, “Rainy Night Scene Understanding with Near-Scene Semantic Adaptation”, IEEE Trans. on Intelligent Transportation Systems, Vol.22, No.3, pp.1594-1602, March 2021. [pdf][code] DOI: 10.1109/TITS.2020.2972912
2020
[15] Ruopei Guo, Chun-Guang Li, Yonghua Li, Jiaru Lin, and Jun Guo, “Density-Adaptive Kernel based Efficient Re-Ranking Approaches for Person Re-Identification”, Neurocomputing, Vol.411, Oct. 2020, pp.91-111.[pdf][DOI][code]
[14] Bo Xiao, Xiang-Yu Li, Chun-Guang Li, Qian-Fang Xu, “A Novel Pooling Block for Improving Lightweight Deep Neural Networks”, Pattern Recognition Letters, Vol.135, July 2020, pp.307-312.
[13] Ruopei Guo, Chaoqun Lin, Chun-Guang Li, and Jiaru Lin, “Deep Group-Shuffling Dual Random Walks with Label Smoothing for Person Re-Identification”, IEEE Access, Vol.8, Feb. 27, 2020, pp.40018-40028. [pdf][code] DOI: 10.1109/ACCESS.2020.2976849
2019
[12] Junjian Zhang, Chun-Guang Li, Tianming Du, Honggang Zhang, and Jun Guo, “Convolutional Subspace Clustering Network with Block Diagonal Prior”, IEEE Access, Vol. 8, Dec. 2019, pp. 5723-5732. [pdf][code] DOI: 10.1109/ACCESS.2019.2963279
2018
[11] Chun-Guang Li, Chong You, and René Vidal, “On Geometric Analysis of Affine Sparse Subspace Clustering”, IEEE Journal of Selected Topics in Signal Processing, Vol.12, Issue 6, pp.1520-1533, Dec. 2018. DOI: 10.1109/JSTSP.2018.2867446 [pdf]
[10] Jianlou Si, Honggang Zhang, Chun-Guang Li, and Jun Guo, “Spatial Pyramid-Based Statistical Features for Person Re-Identification: A Comprehensive Evaluation”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol.48, No.7, pp.1140-1154, July 2018. DOI: 10.1109/TSMC.2016.2645660 [pdf][code]
[9] Shuai Di, Honggang Zhang, Chun-Guang Li, Xue Mei, Danil Prokhorov, and Haibing Ling, “Cross-domain Traffic Scene Understanding: A Dense Correspondence based Transfer Learning Approach”, IEEE Transactions on Intelligent Transportation System, Vol.19, No. 3, pp.745-757, March, 2018. DOI: 10.1109/TITS.2017.2702012 [pdf]
2017
[8] Chun-Guang Li, Chong You, and René Vidal, “Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework”, IEEE Transactions on Image Processing, Vol. 26, No. 6, pp.2988-3001, June 2017. DOI:10.1109/TIP.2017.2691557 [pdf][code][notes]
[7] Xianbiao Qi, Guoying Zhao, Chun-Guang Li, Jun Guo, Matti Pietikainen, “HEp-2 Cell Classification via Combining Multi-resolution Co-occurrence Texture and Large Regional Shape Information”, IEEE Journal of Biomedical and Health Informatics (J-BHI), Vol.21, No.2, pp.429-440, 2017. DOI: 10.1109/JBHI.2015.2508938 [pdf ][code]
2016
[6] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, Available on arXiv: 1605.02633, 2016. [pdf][code]
[5] Chun-Guang Li and Rene Vidal. “A Structured Sparse plus Structured Low-Rank Framework for Subspace Clustering and Completion”, IEEE Transactions on Signal Processing, Vol. 64, No. 24, pp.6557-6570, Dec. 15, 2016. [pdf][code][data][notes] DOI: 10.1109/TSP.2016.2613070
[4] Xianbiao Qi, Chun-Guang Li, Guoying Zhao, Xiaopeng Hong, Matti Pietikainen, “Dynamic texture and scene classification by transferring deep image features”, Neurocomputing, Vol.171, 2016, pp:1230-1241.[pdf]
2014
[3] Xianbiao Qi, Rong Xiao, Chun-Guang Li, Yu Qiao, Jun Guo, and Xiaoou Tang, “Pairwise Rotation Invariant Co-occurrence Local Binary Pattern”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 11, Nov. 2014, pp.2199-2213. DOI: 10.1109/TPAMI.2014.2316826 [pdf][code][ESI高被引论文(Oct.2017)] DOI: 10.1109/TPAMI.2014.2316826
2013
[2] Chun-Guang Li, Zhouchen Lin, and Jun Guo, “Bases Sorting: Generalizing the Concept of Frequency for Over-complete Dictionaries”, Neurocomputing, Vol.115, Sept. 4, 2013, pp.192–200. [pdf][code][data]
2009
[1] Chun-Guang Li, Jun Guo, and Bo Xiao, “Intrinsic Dimensionality Estimation within Neighborhood Convex Hull”, International Journal of Pattern Recognition and Artificial Intelligence, Vol.23, No.1, Feb 2009, pp.31-44. [pdf]
II. 国际会议:
2024
[48] who, who, who, and Chun-Guang Li, "Bla bla bla", submitted to CVPR 2025.
[47] who, who, Chun-Guang Li, and who, "Bla bla bla", submitted to ICLR 2025.
[46] who, who, Chun-Guang Li, and who, "Bla bla bla", submitted to ICLR 2025.
[45] who, who, who, who, and Chun-Guang Li, "Bla bla bla", submitted to ICLR 2025.
[44] who, who, and Chun-Guang Li, "Bla bla bla", submitted to ICASSP 2025.
[43] Qishuai Wen and Chun-Guang Li, "Rethinking Decoders for Transformer-based Semantic Segmentation: Compression is All You Need", Accepted by NeurIPS 2024. [PDF][Openreview][code][poster]
[42] Wei He, Zhiyuan Huang, Xianghan Meng, Xianbiao Qi, Rong Xiao, and Chun-Guang Li, "Graph Cut-guided Maximal Coding Rate Reduction for Learning Image Embedding and Clustering", Accepted by ACCV 2024.
[41] Chen Zhao, Chun-Guang Li, Wei He, and Chong You, "Deep Self-expressive Learning", Proc. of Machine Learning Research, Vol.234, pp.228-247, Conference on Parsimony and Learning (CPAL), Jan.3-6, 2024, Hongkong, P.R. China. [pdf]
2023
[40] Mingkun Li, Shupeng Cheng, Peng Xu, Xiantian Zhu, Chun-Guang Li and Jun Guo, "Unsupervised Long-Term Person Re-Identification with Clothes Change", In Proc. of IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Nov.3-5, 2023, Beijing, P.R. China.
2022
[39] Chao Xue, Xiaoxing Wang, Junchi Yan, Chun-Guang Li, “A Max-Flow based Approach for Neural Architecture Search”, European Conference on Computer Vision (ECCV), Oct. 23-27, 2022, Tel Aviv, Israel. [pdf]
2021
[38] He Sun, Mingkun Li, and Chun-Guang Li, “Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification ”, The 6th Asian Conference on Pattern Recognition (ACPR), Nov. 9-12, 2021, Lecture Notes in Computer Science (LNCS), Vol.13189, pp.532-546.[arXiv][pdf]
[37] Shangzhi Zhang, Chong You, Rene Vidal and Chun-Guang Li, “Learning a Self-Expressive Network for Subspace Clustering ”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 19-25, 2021.[pdf][code][code][arXiv]
[36] Mingkun Li, Ruopei Guo, Chun-Guang Li, and Jun Guo, “Self-paced Bottom-up Clustering Network with Side Information for Person Re-Identification”, International Conference on Pattern Recognition (ICPR), Jan. 10-15, 2021, (Virtual) Milano, Italy.
2020
[35] Chaoqun Lin, Ruopei Guo, Mingkun Li, Xianbiao Qi, and Chun-Guang Li, “Learning Convolution Feature Aggregation via Edge Attention Convolution Network for Person Re-Identification”, IEEE International Conference on Visual Communication and Image Processing (VCIP), Dec.1-4, 2020, (Virtual) Macau, P.R. China.
[34] Ying Chen, Chun-Guang Li, and Chong You, “Stochastic Sparse Subspace Clustering”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.4155-4164.[pdf][arXiv][code: Link-1(passwd:5jre), Link-2]
2019
[33] Tao Yang and Chun-Guang Li, “Local Convex Representation with Pruning for Manifold Clustering”, IEEE International Conference on Visual Communication and Image Processing (VCIP), December 1-4, 2019, Sydney, Australia.[pdf][code][The best student paper award][extended version] DOI:10.1109/VCIP47243.2019.8965757
[32] Chong You, Chun-Guang Li, Daniel P. Robinson, and Rene Vidal, “Is an Affine Constraint Needed for Affine Subspace Clustering?”, IEEE International Conference on Computer Vision (ICCV), 2019, pp.9915-9924, Oct. 27-Nov. 2, 2019, Seoul, Korea. [pdf][code][longer version(with proofs)]
[31] Xianbiao Qi, Yihao Chen, Rong Xiao, Chun-Guang Li, Qin Zou, and Shuguang Cui, “A Novel Joint Character Categorization and Localization Approach for Character-Level Scene Text Recognition”, accepted by International Conference on Document Analysis and Recognition (ICDAR), Workshop on Machine Learning, Sept. 20-25, Sydney, Australia, 2019. [pdf]
[30] Junjian Zhang, Chun-Guang Li, Chong You, Xianbiao Qi, Honggang Zhang, Jun Guo, Zhouchen Lin, “Self-Supervised Convolutional Subspace Clustering Network”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5473-5482, Jun. 16-20, 2019, Long Beach, California, USA. [pdf][code]
2018
[29] Chun-Guang Li, Junjian Zhang, and Jun Guo, “Constrained Sparse Subspace Clustering with Side Information”, International Conference on Pattern Recognition (ICPR), Aug. 20-24, Beijing, 2018, pp.2093-2099.[pdf][slides][code] (oral rate: 10%)
[28] Ruopei Guo, Chun-Guang Li, Yonghua Li, and Jiaru Lin, “Density-Adaptive Kernel Ranking for Person Re-Identification”, International Conference on Pattern Recognition (ICPR), Aug. 20-24, Beijing, 2018, pp.982-987.[pdf][poster][code]
[27] Jianlou Si, Honggang Zhang, Chun-Guang Li, Jason Kuen, Xiangfei Kong, Alex Kot, and Gang Wang, “Dual Attention Matching Networks for Context-Aware Feature Sequence based Person Re-Identification”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 19-21, 2018, Salt Lake City, USA.[pdf]
2016
[26] Junjian Zhang, Chun-Guang Li, Honggang Zhang, and Jun Guo, “Low Rank and Structured Sparse Subspace Clustering”, In Proc. of IEEE International Conference on Visual Communication and Image Processing (VCIP), Nov. 27-30, 2016, Chengdu, China.[pdf]
[25] Chong You, Chun-Guang Li, Daniel Robinson, and Rene Vidal. “Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering”, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vegas, Nevada, US, pp.3928-3937. [pdf][code] (oral, rate: 3.9%)
2015
[24] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo, “Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning”, In Proc. of IEEE International Conference on Computer Vision (ICCV), Dec. 11-18, 2015, pp.2767-2775, Santiago, Chile. [pdf][spotlight][poster][code] (An extended version with theoretical investigation and extensive experimental evaluations will be available soon)
[23] Zhen Qin, Chun-Guang Li, Honggang Zhang, and Jun Guo, “Improving Tag Matrix Completion for Image Annotation and Retrieval”, In Proc. of IEEE International Conference on Visual Communication and Image Processing (VCIP), Dec. 13-16, 2015, Singapore.[pdf]
[22] Chun-Guang Li, Chong You, and René Vidal, “On Sufficient Conditions for Affine Sparse Subspace Clustering”, In Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, July 6-9, 2015, Cambridge, UK.[pdf](The extended version] has been accepted by IEEE Journal of Selected Topics in Signal Processing, July 2018)
[21] Jianlou Si, Honggang Zhang, and Chun-Guang Li, “Regularization in Metric Learning for Person Re-Identification”, In Proc. of IEEE Conference on Image Processing (ICIP), Sept. 27-30, 2015, Quebec, Canada.[pdf]
[20] Chun-Guang Li and René Vidal, “Structured Sparse Subspace Clustering: A Unified Optimization Framework”, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.277-286, June 7-12, 2015, Boston, Massachusetts, US.[pdf][code][notes]
2014
[19] Jianlou Si, Honggang Zhang, and Chun-Guang Li, “Person Re-Identification via Region-of-Interest based Features”, In Proc. of IEEE Conference on Visual Communications and Image Processing (VCIP), Dec.7-10, 2014, Valletta, Malta.[pdf]
2013
[18] Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo, “Exploring Cross-Channel Texture Correlation for Color Texture Classification”, British Machine Vision Conference (BMVC), Sept 9-13, 2013, Bristol, UK. [pdf]
[17] Xianbiao Qi, Yu Qiao, Chun-Guang Li, and Jun Guo, “Multi-scale Joint Encoding of Local Binary Patterns for Texture and Material Classification”, British Machine Vision Conference (BMVC), Sept 9-13, 2013, Bristol, UK. [pdf]
[16] Xianbiao Qi, Yi Lu, Shifeng Chen, Chun-Guang Li, and Jun Guo, “Spatial Co-Occurrence of Local Intensity Order for Face Recognition”, ICME Workshop on Management Information Systems (MIS) in Multimedia Art, Education, Entertainment, and Culture (MIS-MEDIA), July 15-19, 2013, San Jose, USA. [pdf]
[15] Qiang Wang, Zhiyuan Guo, Gang Liu, Chun-Guang Li, Jun Guo, “Local alignment for query by humming”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, May 2013.[pdf]
2012
[14] Chun-Guang Li, Xianbiao Qi, and Jun Guo, “Dimensionality Reduction by Low-Rank Embedding”, The 2012 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (ISciDE2012), LNCS 7751, pp.181-188, 2013. [pdf]
[13] Xianbiao Qi, Rong Xiao, Lei Zhang, Chun-Guang Li, and Jun Guo, “A Rapid Flower/Leaf Recognition System”, The 20th anniversary ACM Multimedia (ACM MM), 2012, Nara. [pdf]
[12] Chun-Guang Li, Xianbiao Qi, Jun Guo and Bo Xiao, “An Evaluation on Different Graphs for Semi-supervised Learning”, The 2011 Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering (ISciDE2011), LNCS 7202, pp. 58-65, 2012. [pdf]
2010
[11] Chun-Guang Li, Jun Guo and Hong-gang Zhang, “Local Sparse Representation based Classification”, The 20th International Conference on Pattern Recognition (ICPR), August 23-26, 2010, Istanbul, Turkey. [pdf][code]
2009
[10] Qianfang Xu, Chun-Guang Li, Bo Xiao, Jun Guo, “A Visualization Algorithm for Alarm Association Mining”, International Conference on Network Infrastructure and Digital Content, pp. 326-330, 2009.[pdf]
[9] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Learning Bundle Manifold by Double Neighborhood Graphs”, The 9th Asian Conference on Computer Vision (ACCV), 2009, LNCS 5996, Part III, pp. 321-330. [pdf][code]
[8] Chuang Zhang, Ming Wu, Chun-Guang Li, Bo Xiao, Zhiqing Lin, “Resume Parser: Semi-structured Chinese Document Analysis”, CSIE (5), 2009, pp.12-16. [pdf]
[7] Hong-gang Zhang, Jun Guo, Guang Chen, and Chun-Guang Li, “HCL2000 — A Large-scale Handwritten Chinese Character Database for Handwritten Character Recognition”, ICDAR 2009, pp.286-290. [pdf][HCL2000下载申请表(Application Form for HCL2000) ][HCL2000 database ][HCL2000 I/O codes] *鼠标停留在下载链接位置会自动提示密码
2007
[6] Chun-Guang Li, Jun Guo, and Xiangfei Nie, “Intrinsic Dimensionality Estimation with Neighborhood Convex Hull”, Proceeding of the International Conference on Computational Intelligence and Security, 2007. [pdf]
[5] Chun-Guang Li, Jun Guo, and Hong-gang Zhang, “Pruning Neighborhood Graph for Geodesic Distance based Semi-Supervised Classification”, Proceeding of the International Conference on Computational Intelligence and Security 2007, pp.428-432. [pdf]
2006
[4] Xiangfei Nie, Jun Guo, Zhen Yang, Chun-Guang Li, Jian Wang, Weihong Deng, “EMD Based Face Gender Discrimination”, The Sixth World Congress on Intelligent Control and Automation (WCICA), Vol.1, pp. 4078-4081, 2006.[pdf]
[3] Chun-Guang Li, Jun Guo, and Xiangfei Nie, “Learning geodesic metric for out-of-sample extension of isometric embedding”, Proceeding of the International Conference on Computational Intelligence and Security 2006, Part I, 2006, pp.449-452. [pdf]
[2] Chun-Guang Li and Jun Guo, “Supervised Isomap with explicit mapping”, Proceeding of the First International Conference on Innovative Computing, Information and Control, Vol.3, 2006, pp.345-348. [pdf][code]
[1] Chun-Guang Li, Jun Guo, Guang Chen, Xiangfei Nie, and Zhen Yang, “A version of Isomap with explicit mapping”, Proceeding of the International Conference on Machine Learning and Cybernetics, Vol.6, 2006, pp.3201-3206. [pdf]
III. 国内期刊:
2008
[3] Bo Xiao, Qian-Fang Xu, Zhiqing Lin, Jun Guo and Chun-Guang Li, “Credible Association Rule and Its Mining Algorithm Based on Maximum Clique”, Journal of Software, Vol.19，No.10，2008，pp.2597-2610.[In Chinese][pdf]
2007
[2] Xiang-Fei Nie, Chun-Guang Li and Jun Guo, “Face recognition based on Gabor wavelet and locally linear embedding”, Computer Engineering and Applications, Vol.43, No. 18, 2007, pp.62-64.[In Chinese]
[1] Xiang-Fei Nie, Chun-Guang Li and Jun Guo, “Face Detection Based on Empirical Mode Decomposition and Matching Pursuit”, Computer Engineering, Vol.33, No.14, July, 2007, pp.30-33.[In Chinese]
IV. 博士学位论文:
[1] 李春光, “流形学习及其在模式识别中的应用 (Manifold Learning and its Applications in Pattern Recognition)”, 北京邮电大学博士学位论文, 2007年12月. [pdf]
V. 课程讲义等:
[1] 李春光, “机器学习与数据科学讲义 (Lecture Notes in Machine Learning and Data Science)”, 正在准备中, 2008年-2017年-2021年-现在. [pdf]
VI. 译著:
[1] 约翰·莱特 (John Wright)，马毅[著]；李春光，袁晓军，高盛华 译，基于低维模型的高维数据分析：原理、计算和应用 (High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications), 北京: 机械工业出版社, 2024年8月. [机械工业出版购买链接][英文版购买链接][下载链接]
VII. 专利:
[1] 张政波，梁洪，袁田，李春光，韩良松，时颖: 一种对状态空间聚类提升状态辨识水平的方法, 申请号: CN 2024113158708. (公布，发明专利)
[2] 张政波，李春光，时颖，韩良松，袁田: 一种识别人体所处生理稳态和过渡态的方法, 申请号: CN 2024113151130. (公布，发明专利)
VIII. 其它:
2024
[3] 邱丽叡，李春光，“带自适应数据插补和影响衰减的时序预测聚类算法”，中国科技论文在线，2024年5月10日.
2023
[2] 范晓翰，黄致远，李春光，“自适应提升的正交匹配寻踪稀疏子空间聚类”，中国科技论文在线，2023年5月19日.
[1] 周英杰，李春光，“自增强的深度子空间聚类”，中国科技论文在线, 2023年4月17日.
外语能力:
[2] 第2外语: 英语 [全国英语等级考试 5级*] (2011.06) *: 最高级 (top level)
[1] 第1外语: 日语 [日语国际水平测试 1级*] (2006.02) *: 最高级 (top level)
讲授课程
1. 离散数学(本科生) [2008 春季]
2. 数字信号处理(本科生) [2009 秋季]
3. 生物信息基础(本科生) [2012春] [2014秋-2018秋][2020-2023春][2024春季][每周二 上午 1-2节 8:00-9:35 第1-16周@教二-201教室 ]
本课程定位于介绍分子生物数据的计算机自动分析与处理方法，侧重于介绍生物信息学中的基本问题、数学模型及算法。通过本课程的学习，使学生掌握分子生物数据分析与处理的基本方法，特别是处理序列与阵列数据的方法，培养学生分析问题和解决问题的能力，也有助于学生加深前续基础课程所接触的数学概念和理论的理解和运用，并为后续从事相关领域学习、科学研究和技术开发铺垫基础。授课内容范围：生物学基础（DNA的结构和功能以及相关概念），常用的生物分子数据库，序列比对算法及其应用，基因组结构与基因识别，隐马尔科夫模型，系统进化树分析，蛋白质结构预测，基因表达数据分析及应用等。 *其他推荐阅读资料
[0] 课程介绍 [slides]
[1] 绪论 [slides]
[2] 生物学基础回顾 [slides]
[3] 数据库介绍 [slides]
[4] 序列分析 [slides]
[5] 系统发育分析 [slides]
[6] 基因识别与基因组分析 [slides]
[7] 隐马尔科夫模型 [slides]
[8] 蛋白质结构预测 [slides]
[9] 基因表达谱分析 [slides]
[10] 课程小结与结束语 [slides]
* 旧课件下载链接：[slides]
4. 人工智能导论 (本科生 大一) [2020-2022年秋] [2023秋] 机器学习导论 3次课 [10月27-11月10日] 沙河校区 周五13:00-14:35 N513
本课程定位于给学生提供对于人工智能中的模式识别和机器学习的感性认识，对后续的基础课程学习和专业课程学习提供若干有益的建议，并且揭示基础课程和专业课程中的知识点与人工智能、模式识别和机器学习的内在关联, 进而实现"导兴趣"和“导认识”的课程设置目标. 课件下载链接如下:
[1] 人工智能导论 之 机器学习引论 I：模式识别引例 [slides]
[2] 人工智能导论 之 机器学习引论 II: 机器学习概念介绍 [slides]
[3] 人工智能导论 之 机器学习引论 III: 机器学习算法入门 [slides]
5. 模式识别引论(研究生) [2014 秋季]
[0]课程介绍
[1]引言 ( slides )
[2]部分数学基础 ( slides )
[3]线性回归 ( slides A , slides B)
[4]线性模型for分类 ( slides )
[5]神经网络 ( slides )
[6]核方法 ( slides )
[7]支持向量机 ( slides )
[8]其它 (聚类\降维等) ( slides)
6. 神经计算(研究生) [2008-2012 春季][2014-2015 春季][2016 春季][2017年改为“机器学习与数据科学”]: This course covers the most machine learning techniques. Topics can be divided into three categories: 1) the classical topics of neural networks (e.g., Perceptron, Multi-layer Perceptron, Regularized Networks, and Self-Organization Map, and Deep Networks); 2) a series of statistical learning theories (e.g., the classical error analysis via bias-variance decomposition, Vapnik’s statistical learning theory and VC-dimension, regularization theory); 3) the related learning machines/algorithms (e.g., bagging, boosting/AdaBoost, Mixture of Experts, Decision Tree, Support Vector Machine, Kernel Methods, and Kernel Machine, Regularization networks), and other active topics in recent machine learning community (e.g., Manifold Learning, Subspace Clustering, Compressed Sensing, Sparse Representation, Low-Rank model, Matrix Completion and Sensing).
7. 模式识别与机器学习【2021版信通专业新大纲把“模式识别与机器学习”变更为“机器学习”】[ 2018秋 ][ 2019秋(前1-10周 passwd:r242) ][2020年秋][ 2021年秋 (passwd: m4s2)][2022年秋][2023年秋][2024年秋 (课程编号: 3111100941): 每周五9:50-11:25, 教三237，欢迎选课或旁听~~ 模式识别与机器学习(2)班 ]
授课定位：面向对人工智能、机器学习、模式识别、数据挖掘等领域感兴趣的硕士研究生和博士研究生；强调基本概念、基本模型和数学推导，同时欢迎数学基础较好的高年级本科生旁听。
授课内容：部分数学基础(概率论、决策论和信息论) / 线性回归 / 线性分类 / 神经网络 / 核方法 / 支持向量机 / 概率图模型 / 聚类 / 混合模型 / 采样方法 / 降维等。授课内容将尝试更新到最新版本的Textbook框架上。
参考教材：
[1] Christopher M. Bishop, "Pattern Recognition and Machine Learning"，Springer 2006. [PDF][homepage]
[2] Christopher M. Bishop and Hugh Bishop, "Deep Learning: Foundations and Concepts"，Springer 2024. [homepage]
旧课程资源下载([Slides [下载]] [ Homeworks ][ Others])
[0] 课程介绍 ( slides )
[1] 引言 ( slides )
[2] 部分数学基础 ( slides 1) ( slides 2) ( slides 3) ( slides 4) [作业1]
[3] 线性模型用于回归——单层神经网络 ( slides ) [作业2]
[4] 线性模型用于分类——单层神经网络 ( slides )
[5] 神经网络 (slides )
[6] 核方法 (slides )
[7] 支持向量机 (slides )
[8] 概率图模型 (slides )
[9] 混合模型与EM算法 (slides )
[10] 近似推理 / 采样方法 / 隐变量模型 (slides)
[11] 课程结束语
8. 机器学习与数据科学 【2021版信通专业新大纲中把“机器学习与数据科学”变更为“数据科学”】 [2017-2021春]
[课程教学要求]：覆盖机器学习与数据科学领域的典型算法，从最基本的学习算法——如最近邻、线性回归和感知器等——到支持向量机、深度学习，同时涵盖流形学习、压缩感知等无监督学习方面的最新进展。除了介绍应用和算法之外，本课程强调与算法相关的理论部分的介绍。在理论方面，涵盖参数估计、偏倚方差分解、统计学习理论和正则化理论；在学习范式方面，涵盖有监督学习、无监督学习与半监督学习等；在课程内容设置上，力争衔接基本概念、经典理论和研究前沿。希望通过本课程的学习，为今后有志于在模式识别、机器学习、数据科学等领域内从事相关研究的研究生打下坚实基础。
专题 0: 课程介绍/机器学习发展简史/典型问题举例 [课件下载]
专题 1: 基于实例的学习 [课件下载]
专题 2: 线性模型 [课件下载]
专题 3: 线性模型的扩展 [课件下载 I II III]
专题 4: 学习过程的统计性质 [课件下载 I II]
专题 5: 支持向量机与统计学习理论 [课件下载 I II]
专题 6: 正则化理论及其应用 [课件下载 I II]
专题 7: 无监督学习与半监督学习 [课件下载 I II III]
专题 8: 压缩感知与稀疏表示 [课件下载 ]
专题 9: 处理大规模数据的策略
* 往年课件[下载链接] / 本课程讲义: 《机器学习与数据科学讲义》(2008-2013-Now)正在准备中…..
* 其他推荐资料 ( Download 链接2 ) [提示: 鼠标滑过左侧下载链接，密码自动显示]
9. 数据科学 [2022-2023年春][2024年春 教3-235/ 每周五 上午 3-5节 9:50-12:15 / 48 学时 / 3学分 / 课程微信群(待公布) / 腾讯会议链接 ] [*2022-2023信通学科研究生核心前沿课程建设项目]
[课程教学要求]：本课程讲授数据科学与机器学习领域的典型模型、算法及理论，特别是涉及高维数据的建模、分析与处理的专业基础知识。内容涵盖基于实例的学习、线性模型及其扩展、集成学习、子空间与流形学习、聚类分析、压缩感知(稀疏表示/矩阵补全及矩阵恢复)等模型和算法，包括偏倚方差分解、VC维统计学习理论、正则化理论、高维空间几何、低秩数据发现与恢复、高维空间中低维结构检测等典型理论。本课程旨在为准备在数据科学、机器学习、模式识别、数据挖掘、大数据及物联网等智能信息处理等方向开展研究的学术型研究生(特别是博士生)提供较为系统和完善的专业理论基础。课程侧重于面向高维数据的建模、分析与处理的模型、算法和相应的理论结果，在介绍基本概念和理论的同时，力争衔接前沿研究进展。
专题 0: 课程介绍 绪论 [下载]
专题 1: 基于实例的学习 [下载]
专题 2: 线性模型 [下载]
专题 3: 线性模型的扩展 [下载]
专题 4: 学习过程的统计性质及应用 [下载]
专题 5: 统计学习理论及应用 [下载]
专题 6: 正则化理论及应用 [下载]
专题 7: 数据降维与可视化 [下载]
专题 8: 聚类分析与半监督学习 [下载]
专题 9: 压缩感知与稀疏表示 [下载]
专题 10: 处理大规模数据的策略
* 本课程讲义: 《机器学习与数据科学讲义》(2008-2013-2021-Now)正在准备中
* 往年课件下载: [课件下载]
指导与协助指导的学生 (欢迎做事踏实认真、基础扎实、对机器学习/数据科学/生物信息学/精准医学等相关方向硬核学术研究的兴趣与热爱真挚的同学们提前与我联系~~(非诚勿扰! Sept 28, 2021) 申请攻读免试硕士研究生或博士研究生的主要考核要素: ①扎实的数学基础与熟练的编程能力; ②较好的英语基础和自学能力; ③沉着而孜孜以求的踏实做事风格; ④良好的心态调节能力和对科学研究的兴趣与热爱 *注: ①+②:研究实习 / ①+②+③:攻读学硕 / ①+②+③+④:攻读博士)
1. 所指导和协助指导的硕士和博士研究生
2024.09 – present: [硕] 罗云浩，闻其帅；[博] 黄致远
2023.10 – present: [博] 赵钰莹
2023.10 – 2024.06: [硕] 洪世勇
2023.09 – 2024.06: [硕] 黄致远
2022.09 – present: [硕] 郭卓远，余德健； [博] 孟祥涵
2021.09 – 2024.07: [硕] 邱丽叡
2021.09 – 2024.06: [硕] 赵晨
2021.09 – present: [硕] 童政钰； [博] 何为
2020.09 – present: [博] 薛超
2020.09 – 2023.06: [硕] 范晓翰，周英杰
2019.09 – 2022.06: [硕] 孙赫，张尚之
2018.09 – 2022.06: [博] 李鸣坤 (with 郭军 教授)
2017.09 – 2020.06: [硕] 杨涛
2016.10 – 2017.06: [博] 狄帅 (with 张洪刚 副教授)
2015.11 – 2020.08: [博] 郭若沛 (with 林家儒 教授)
2014.10 – 2020.05: [博] 张军建 (with 郭军 教授)
2014.04 – 2020.05: [博] 秦臻 (with 郭军 教授)
2014.01 – 2018.06: [博] 四建楼 (with 张洪刚 副教授)
2009.09 – 2010.05: [硕] 顾芳 (with 郭军 教授)
2008.09 – 2014.12: [硕][博] 齐宪标 (with 郭军 教授)
2. 所指导的本科生
2025: 陈誉，李恒屹，刘开来，杨梦明
2024: 廖星，闻其帅，許璟昊，郑寒璐
2023: 黄致远，汪子涵，徐成健，郑子楠，朱楠
2022: 白雨田，李宁，刘梓杉，唐江南，余德健
2021: 邱丽叡，赵晨
2019: 邵嘉伟，时尚昊，孙赫，张尚之
2017: 罗毅超, 武瑞, 杨涛
2013*: (未指导毕设: JHU访学)
2012*: (未指导毕设: MSRA铸星计划/JHU访学)
2011*: (未指导毕设: MSRA铸星计划)
2010: 成林, 杨志诚, 崔子腾, 李昂然, 葛晗, 马淑靖, 徐饶, 甘强科
2009: 陈亮, 卢厚祥
2008: 齐宪标, 李晖, 刘乐凯, 唐寿成
学术服务与会员:
[7] 担任审稿人(Reviewer): JMLR, NSR, Nature: Mach. Intell., IJCV, IEEE TPAMI / J-STSP / TSP / TIP / TNNLS / TKDE / TCYB / TSMC / TBME / TCBB / TCSVT / Access / SPL, ACM TKDD, PR*, Neural Networks, Neurocom.*, SP:Image Comm.*, Info. Sciences, PRL, DMKD, DATAK, AI Review, Soft Computing, IJBC, NCAA, BDIA, IJPRAI, ICML, NeurIPS, SPARS, ICCV, CVPR**, ICLR, AISTATS, ECCV, WACV, AAAI, IJCAI**, ACM MM, ICPR**, 《电子学报》, 《自动化学报》, 《计算机辅助设计与图形学学报》, 《控制与决策》, 《北京理工大学学报》,《东南大学学报》,《华南理工大学学报》,《北京邮电大学学报》, PRCV, CCDM, ISciDE, IC-NIDC
* Outstanding Reviewer Status Achieved (2017) / ** Area Chair或SPC
[6] 担任领域主席(Area Chair): ICPR(2020) / CVPR(2021) / SPC for IJCAI(2021)
[5] 中国计算机学会(CCF)人工智能专委会(CCFAI)专委会执行委员(2019.08-present)
[4] 中国图像图形学会机器视觉(CSIG-MV)专委会执行委员(2017.08-present)
[3] 中国计算机学会计算机视觉(CCF-CV)专业委员会执行委员(2016.09-present)
[2] IEEE高级会员(Senior Member) (2021.04-present)
[1] ACM会员 (2018.01-2022.12) /CCF会员 / CSIG会员
学术报告(Talks)/科普讲座/Tutorials:
[21] 学术报告：Deep Self-Expressive Learning, at 中国科学院数学与系统科学研究院 系统科学研究所，非线性代数与数据科学研讨会(Nonlinear Algebra and Data Science Seminar)系列讲座 , 2024. [TBD]
[20] 学术报告：Self-Expressive Models for Clustering High Dimensional Data (自表达模型用于聚类高维数据), at 北京邮电大学 人工智能学院 人工智能学术论坛第71期, 腾讯会议 (ID: 637 6868 7744 )，Oct. 23, 2024.
[19] 学术报告：Self-Expressive Models for Clustering High-Dimensional Data, at 中国科学院数学与系统科学研究院 应用数学研究所, August 14, 2024.
[18] 学术报告：Self-Expressive Learning, at 东莞理工学院 计算机科学与技术学院，腾讯会议(ID: 996 979 1939)，Dec. 22, 2023.
[17] 学术报告：Learning Complex Low Dimensional Structures in High Dimensional Data, at 西北民族大学图像智能分析与应用国际学术研讨会, Sept 28, 2022.
[16] 学术报告：Self-Expressive Learning, at 解放军总医院医学人工智能中心, July 29, 2022.
[15] 学术报告：Learning Low Dimensional Structures in Data via Self-Expressive Models, at 北京理工大学, Jan. 5, 2022.
[14] 学术报告：Pursuing Low-Dimensional Structures in Data via Self-Expressiveness, at 深圳大学数学与统计学院, 腾讯会议(ID: 713 452 447), Oct. 27, 2021.
[13] 学术报告：Pursuing Low-Dimensional Structures from High Dimensional Data (追踪高维数据中的低维结构), at 北京理工大学自动化学院，July 10, 2021.
[12] [微软亚洲研究院(MSRA)创研论坛——CVPR2021论文分享会] “Learning a Self-Expressive Network for Subspace Clustering”, April 22, 2021.[视频回放]
[11] 学术报告：Pursuing Low-Dimensional Structures from High Dimensional Data, at 平安财产险科技中心人工智能部，Dec. 29, 2020.
[10] [微软亚洲研究院(MSRA)创研论坛——CVPR2020论文分享会] “Stochastic Sparse Subspace Clustering”, May 14, 2020.[视频回放]
[9] ICCV2019中国预会议(pre ICCV2019), “Is an Affine Constraint Needed for Affine Subspace Clustering?”, at 北京大学秋林报告厅, Sept 19, 2019.
[8] 中国计算机学会人工智能会议(CCFAI 2019) 聚类分析学术专题论坛—— “子空间聚类研究进展——算法、理论及应用”(Subspace Clustering: Recent Advances in Algorithms, Theories & Applications), at 徐州市博顿温德姆酒店, August 20, 2019.
[7] 九三学社海淀区”科创吧”青年学术沙龙系列活动 (首场) “子空间聚类的研究进展——算法、理论以及应用”(Subspace Clustering: Recent Advances in Algorithms, Theories & Applications), at 北京邮电大学 教一1层116会议室，12:00-13:30, April 24, 2019.
[6] [微软亚洲研究院(MSRA)创研论坛——CVPR2019论文分享会] Self-Supervised Convolutional Subspace Clustering Network, at 清华大学罗姆楼3层报告厅，April 2, 2019.
[5] Tutorial: “Subspace Clustering: Recent Advances in Algorithms, Theories and Applications”, Joint with Chong You, Guangcan Liu, Risheng Liu at International Conference on Pattern Recognition (ICPR), August 20, 2018.[时间: 14:00-17:00, 地点: 北京国家会议中心三层306A][download (passwd: yygd)]
[4] 学术报告：Structured Sparse Subspace Clustering and Some Extensions, at 天津大学, 世界智能大会—天津大学机器视觉与学习专题论坛, May 17, 2018.
[3] [CSIG-MV系列科普讲座] 智造未来: 从中国制造2025谈人工智能, at 人大附中北京经济技术开发区实验学校，April 19, 2018.
[2] 学术报告：结构化子空间聚类及其扩展 (带辅助信息 / 半监督 / 缺失值), at 山东财经大学 计算机科学与技术学院, Dec. 16, 2017.
[1] 学术报告：Structured Sparse Subspace Clustering and Some Extensions, at SIST, ShanghaiTech(上海科技大学)，Nov.27, 2017.
其它社会活动:
[1] 九三学社社员 (2009.12-present)/九三学社北邮委员会第三支社组织委员(2018.09-现在)
其它获奖/个人链接/业余活动:
[4] 阅读 / 乒乓球(右手横板双反快攻) / 音乐 / 摄影
[3] 谷歌学术(Google Scholar) / ResearchGate / 领英LinedIn / QQ空间
[2] 校田径运动会(凌源一中/吉林大学南湖校区/北京邮电大学) 5000m/1500m冠军多次
[1] 1998.09-2002.07: 吉林大学南湖校区(原 长春邮电学院)6次荣获一等奖学金
其它资源链接:
[24] 颜宁的三点做研究总结 – [link]
[23] 沈向洋博士演讲系列: [You are how you read@GIX][三十年科研路我踩过的那些坑@X-talk]
[22] 施一公教授在2018年全国科学道德和学风建设宣讲教育报告会上的演讲《做诚实的学问、做正直的人》 – [演讲稿全文][含演讲现场视频]
[21] 为纯科学而呼吁 (A Plea for Pure Science) (by Henry A.Rowland) – [link][pdf][English Version][亨利·罗兰生平]
[20] 汉明的演讲：你和你的研究 (You and your research, by Richard Hamming) – [link][pdf][Hamming’s Advice]
[18] 如何做研究 – [link]
[17] Modeling with High Dimensional Data: [subspace clustering] [Scalable Sparse Subspace Clustering] [Sparse and Low-Rank Model for Visual Analytics] [link]
[16] Manifold Learning Resource – [ISOMAP][LLE][LaplacianEigenmap][ManifoldCharting][HessianLLE][LTSA][SDE][Logmap][DiffusionMaps][spectralmethod][comparison][survey]
[15] Research Links: Machine Learning/Compressed Sensing /Action /Activity /Feature /Optimization/Subspace – [link]
[14] Machine Learning (Theory) – [link]
[13] Resource on Sensing and Analysis of High-dimensional Data – [link] [link]
[12] Compressed Sensing Resource – [link] [link]
[11] Preprint Papers – [link]
[10] Dodo’s Pattern Recognition Commune – [link]
[9] The Psychology of Luck – [link]
[8] Microsoft Academic Search – [link]
[7] List of Computer Science Conference – [link]
[6] Comments on SCI-Journals – [link]
[5] List of some journal impact factors – [link]
[4] Academic Lecture Videos – [link]
[3] Mathematics – Stack Exchange – [link]
[2] Ten Simple Rules for Mathematical Writing – [link]
