刘光灿/ 男  博士  教授  博士生导师,硕士生导师,国家级人才,全体教师
姓名:刘光灿
地点:四牌楼校区智能控制楼305
电话:
教师主页:https://github.com/gcliu1982
邮箱:guangcanliu@seu.edu.cn
基本信息

刘光灿，男，教授，主要研究低维模型与优化在机器学习、模式识别、计算机视觉、信号处理、序列预测等领域的应用。在高维数据的无监督、无损、低维表达方面取得了重要进展：提出低秩表示及其可学习化模型与理论，引领了无监督无损表示学习这一研究方向。发表IEEE T-IT、IEEE T-PAMI、NeurIPS、ICML等CCF A类论文50余篇，Google Scholar 引用10000多次。2016年获国家优青与江苏省杰青；2017年获教育部自然科学二等奖与吴文俊人工智能优秀青年奖，并入选科睿唯安ESI高被引学者榜单；2020年获CCF自然科学一等奖；2021年获中国电子学会自然科学一等奖。现为IEEE高级会员，担任ICML、NeurIPS等CCF A类会议的Area Chair。

教育背景

  • 2004.9-2010.7, 上海交通大学, 计算机应用技术, 博士，导师：俞勇、林宙辰、汤晓鸥

  • 2000.9-2004.7, 上海交通大学, 数学与应用数学, 学士

  • 1997.9-2000.7, 湖南省隆回县第一中学，高中

工作经历

  • 2022-01 至 今, 东南大学, 自动化学院, 教授

  • 2015-08 至 2021-12, 南京信息工程大学, 自动化学院, 教授

  • 2014-08 至 2015-08, 南京信息工程大学, 自动化学院, 校聘教授（讲师）

  • 2013-07 至 2014-08, 康奈尔大学，Research Associate，合作导师：Ping Li

  • 2012-02 至 2013-02, 伊利诺伊大学香槟分校，Research Associate，合作导师：Yi Ma

  • 2010-09 至 2011-12, 新加坡国立大学，Research Fellow，合作导师：Shuicheng Yan

学术兼职

  • 2022、2023年，担任国际会议ICML的Area Chair

  • 2022、2023年，担任国际会议NeurIPS的Area Chair

  • 2021年，担任国际会议IJCAI的Senior PC Member

  • 2019、2020年，担任国际会议AAAI的Senior PC Member

  • 2020、2022年，担任国际会议ICPR的Associate Editor

  • 2020、2022年，担任国际会议ACCV的Area Chair

  • 2018年至今，担任中国图象图形学学会---机器视觉专委会的委员、副秘书长

  • 2015年至2020年，担任国际期刊Neurocomputing的Associate Editor

  • 2023年至今，担任国际期刊Visual Intelligence的Associate Editor

所获奖励

  • 2021年，获中国电子学会自然科学一等奖（排2）

  • 2020年，获CCF自然科学一等奖（排2）

  • 2017年，获吴文俊人工智能优秀青年奖

  • 2017年，获教育部自然科学二等奖（排2）

  • 2017年，入选科睿唯安ESI全球高被引学者

  • 2016年，获国家优青与江苏省杰青

  • 2015年，获上海市研究生优秀成果奖

讲授课程

  • 2015-2016，《矩阵论》

  • 2016-2017、2017-2018、2020-2021，《数据库技术与应用》

  • 2017-2018，《矩阵分析》

  • 2018-2019、2022-2023，《人工智能导论》

  • 2021-2022，《机器学习》

学生培养

  • 博士研究生：

  • 高思楠，2018级

  • 张芮，2018级

  • 朱轶昇，2020级

  • 杜凯乐，2023级


  • 硕士研究生：

  • 杨毅， 2016级

  • 刘光宇，2017级

  • 袁权，2017级

  • 张令威，2017级

  • 吴哲夫，2017级

  • 朱轶昇，2017级

  • 王心，2018级

  • 丁雪琴，2018级

  • 赵琦，2018级

  • 朱浩华，2018级

  • 管振玉，2019级

  • 王子琦，2019级

  • 徐杰杰，2020级

  • 胡高杰，2020级

  • 李继文，2020级

  • 郭泽斌，2020级

  • 王文青，2020级

  • 贾子豪，2021级

  • 孙圣锟，2021级

  • 李雨阳，2022级

  • 李卫，2022级

  • 余意，2023级

研究兴趣

低维模型与优化在机器学习、模式识别、计算机视觉、信号处理、序列预测等领域的应用

科研项目

  • 2018AAA0102501，真实世界情境感知与理解的模型与方法，国家重点研发计划科技创新2030新一代人工智能重大项目，2019-2023， 455万，课题负责人，在研。

  • 2018AAA0100601，跨媒体常识的形成机理建模及其表达，国家重点研发计划科技创新2030新一代人工智能重大项目，2019-2022， 47万，子课题负责人，在研。

  • 基于深度学习的视频预测关键技术研发，商汤科技有限公司横向项目，2018 - 2019， 20万，项目负责人，结题。

  • NSFC61622305，低秩学习与视觉分析, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万，项目负责人，结题。

  • BK20160040，面向大数据的低秩学习方法, 江苏省自然科学基金杰出青年基金项目, 2017 - 2019, 100万，项目负责人，结题。

  • NSFC61502238，基于字典学习的低秩矩阵恢复方法研究, 国家自然科学基金青年项目, 2016 - 2018, 24.4万, 项目负责人，结题。


论文发表

[77] Yishang Zhu, Hui Shuai, Guangcan Liu*, Qingshan Liu, Multilevel Spatial–Temporal Excited Graph Network for Skeleton-Based Action Recognition, IEEE Transactions on Image Processing (T-IP), vol. 32, pp. 496-508, 2023. 

[76] Zhili Zhou*, Chun Ding, Jin Li, Eman Mohammadi, Guangcan Liu, Yimin Yang, QM Jonathan Wu, Sequential Order-Aware Coding-Based Robust Subspace Clustering for Human Action Recognition in Untrimmed Videos, IEEE Transactions on Image Processing (T-IP), vol.32, pp. 13-28, 2023. 

[75] Guangcan Liu*, Wayne Zhang, Recovery of future data via convolution nuclear norm minimization, IEEE Transactions on Information Theory (T-IT), vol. 69, no. 1, pp. 650-665, 2023. 

[74] Xingyu Xie, Qiuhao Wang, Zenan Ling, Xia Li, Guangcan Liu, Zhouchen Lin*, Optimization Induced Equilibrium Networks: An Explicit Optimization Perspective for Understanding Equilibrium Models, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2022. 

[73] Yisheng Zhu, Hui Shuai, Guangcan Liu*, Qingshan Liu, Self-Supervised Video Representation Learning using Improved Instance-wise Contrastive Learning and Deep Clustering, IEEE Transactions on Circuits and Systems for Video Technology (T-VSCT), vol. 32, no. 10, pp. 6741-6752, 2022. 

[72] Guangcan Liu*, Time Series Forecasting via Learning Convolutionally Low-Rank Models, IEEE Transactions on Information Theory (T-IT), vol.68, no.5, pp. 3362-3380, 2022. 

[71] Rui Zhang, Qingshan Liu*, Renlong Hang, Guangcan Liu, Predicting Tropical Cyclogenesis Using a Deep Learning Method From Gridded Satellite and ERA5 Reanalysis Data in the Western North Pacific Basin, IEEE Transactions on Geoscience and Remote Sensing (T-GRS), vol. 60, pp. 1-10, 2022. 

[70] Zhao Zhang*, Jiahuan Ren, Haijun Zhang, Zheng Zhang, Guangcan Liu, Shuicheng Yan, DLRF-Net: A Progressive Deep Latent Low-Rank Fusion Network for Hierarchical Subspace Discovery, ACM Transactions on Multimidia Computing Communications and Applications (TOMM), vol. 17, no. 1, pp. 1-24, 2021. 

[69] Yang Li*, Guangcan Liu, Yubao Sun, Qingshan Liu, Shengyong Chen, 3D Tensor Auto-encoder with Application to Video Compression, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 17, no.2, pp. 1-18, 2021. 

[68] Yisheng Zhu, Hu Han, Guangcan Liu (*), Qingshan Liu, Collaborative Local-Global Learning for Temporal Action Proposal, ACM Transactions on Intelligent Systems and Technology (TIST), vol.12, no. 5, pp. 1-14, 2021. 

[67] Qi Zhao, Guangcan Liu*, Qingshan Liu, Tensor LISTA: Differentiable sparse representation learning for multi-dimensional tensor, Neurocomputing, vol. 463, pp. 554-565, 2021.  

[66] Qi Zhao, Chuqiao Chen, Guangcan Liu *，Qingshan liu, Shengyong Chen, Parallel Connected LSTM for Matrix Sequence Prediction with Elusive Correlations, ACM Transactions on Intelligent Systems and Technology (TIST), vol. 12, no. 4, pp. 5101-5116, 2021.

[65] Zhao Zhang *, Yulin Sun, Yang Wang, Zheng Zhang, Haijun Zhang, Guangcan Liu, Meng Wang, Twin-incoherent self-expressive locality-adaptive latent dictionary pair learning for classification, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol. 32, no. 3, pp. 947-961, 2021.

[64] Guangcan Liu*, Qingshan Liu, Xiao-Tong Yuan, Meng Wang, Matrix Completion with Deterministic Sampling: Theories and Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 43, no. 2, pp. 549-566, 2021.

[63] Xingyu Xie, Hao Kong, Jianlong Wu, Wayne Zhang, Guangcan Liu*, Zhouchen Lin*, Maximum-and-concatenation networks, International Conference on Machine Learning (ICML), pp. 10483-10494, 2020. 

[62] Xin Wang, Guangcan Liu*, Wayne Zhang, Qingshan Liu, Selecting optimal completion to partial matrix via self-validation, IEEE Signal Processing Letters (SPL), vol. 27, pp. 1265-1269, 2020. 

[61] Jiahuan Ren, Zhao Zhang*, Sheng Li, Yang Wang, Guangcan Liu, Shuicheng Yan, Meng Wang, Learning hybrid representation by robust dictionary learning in factorized compressed space, IEEE Transactions on Image Processing (T-IP), vol. 29, pp. 3941-3956, 2020. 

[60] Zhao Zhang*, Jiahuan Ren, Zheng Zhang, Guangcan Liu, Deep latent low-rank fusion network for progressive subspace discovery, International Joint Conferences on Artificial Intelligence (IJCAI) , pp. 2762-2768, 2020. 

[59] Yisheng Zhu*, Guangcan Liu, Fine-grained action recognition using multi-view attentions, The Visual Computer, 2019.

[58] Zhao Zhang*, Weiming Jiang, Zheng Zhang, Sheng Li, Guangcan Liu, Jie Qin. Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning. International Joint Conference on Artificial Intelligence (IJCAI), pp.4376-4382, 2019.

[57] Zhao Zhang*, Yan Zhang, Sheng Li, Guangcan Liu, Meng Wang, Shuicheng Yan, Robust Unsupervised Flexible Auto-weighted Local-coordinate Concept Factorization for Image Clustering, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2092-2096, 2019. 

[56] Lei Wang, Bangjun Wang, Zhao Zhang*, Qiaolin Ye, Liyong Fu, Guangcan Liu, Meng Wang, Robust auto-weighted projective low-rank and sparse recovery for visual representation, Neural Networks, vol. 117, pp. 201-215,2019.

[55] Zhao Zhang*, Yan Zhang, Sheng Li, Guangcan Liu, Dan Zeng, Shuicheng Yan, Meng Wang, Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering, IEEE Transactions on Knowledge and Data Engineering (T-KDE), 1523 – 1539, 2021.

[54] Xueliang Liu, Rongjie Zhang, Zhijun Meng, Richang Hong, Guangcan Liu, On fusing the latent deep CNN feature for image classification, World Wide Web (WWW), vol. 22, no. 2, pp. 423-436, 2019. 

[53] Xianglin Guo, Xingyu Xie, Guangcan Liu, Mingqiang Wei, Jun Wang*, Robust Low-rank subspace segmentation with finite mixture noise, Pattern Recognition, vol.93, pp. 55-67, 2019. 

[52] Guangcan  Liu*, Zhao Zhang, Qingshan Liu, Hongkai Xiong, Robust Subspace Clustering with Compressed Data, IEEE Transactions on Image Processing (T-IP), vol. 28, no. 10, pp. 5161-5170, 2019. 

[51] Xingyu Xie, Jianlong Wu, Zhisheng Zhong, Guangcan Liu*, Zhouchen Lin*, Differentiable Linearized ADMM, International Conference on Machine Learning (ICML), pp. 6902-6911, 2019. 

[50] Zhao Zhang, Yan Zhang, Guangcan Liu, Jinhui Tang, Shuicheng Yan, Meng Wang, Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation. IEEE Transactions on Knowledge and Data Engineering (T-KDE), vol. 32, no.5, pp.952-970, 2020. 

[49] Xingyu Xie, Jianlong Wu, Guangcan Liu, Jun Wang, Matrix Recovery with Implicitly Low-Rank Data. Neurocomputing, vol. 334, pp. 219-226, 2019. 

[48] Yang Li*, Guangcan Liu, Qingshan Liu, Yubao Sun, Shengyong Chen. Moving object detection via segmentation and saliency constrained RPCA. Neurocomputing, vol. 323, pp. 352-362, 2019.

[47] Min Wu, Yubao Sun, Renlong Hang, Qingshan Liu, Guangcan Liu, Multi-Component Group Sparse RPCA Model for Motion Object Detection under Complex Dynamic Background. Neurocomputing, 2018.

[46] Zhao Zhang, Weiming Jiang, Sheng Li, Jie Qin, Guangcan Liu, Shuicheng Yan, Robust Locality-Constrained Label Consistent K-SVD by Joint Sparse Embedding, International Conference on Pattern Recognition (ICPR), pp. 1664-1669, 2018.

[45] Zhao Zhang, Weiming Jiang, Sheng Li, Jie Qin, Guangcan Liu, Shuicheng Yan, Robust Discriminative Projective Dictionary Pair Learning by Adaptive Representations, pp. 621-626, International Conference on Pattern Recognition (ICPR), pp. 800-805, 2018.

[44] Lei Wang, Zhao Zhang, Guangcan Liu, Qiaolin Ye, Jie Qin, Meng Wang. Robust Adaptive Low-Rank and Sparse Embedding for Feature Representation, International Conference on Pattern Recognition (ICPR), pp. 800-805, 2018.  

[43] Yan Zhang, Zhao Zhang,Sheng Li, Jie Qin, Guangcan Liu, Meng Wang, Shuicheng Yan. Unsupervised Nonnegative Adaptive Feature Extraction for Data Representation, IEEE Transactions on Knowledge and Data Engineering (T-KDE), vol.31, no. 12, pp. 2423-2440, 2019. 

[42] Zhengbo Yu, Guangcan Liu, Qingshan Liu, Jiankang Deng. Spatio-Temporal Convolutional Features with Nested LSTM for Facial Expression Recognition. Neurocomputing, vol.137, pp.50-57, 2018. 

[41] Lei Wang, Zhao Zhang, Sheng Li, Guangcan Liu, Chenping Hou, Jie Qin. Similarity-Adaptive Latent Low-Rank Representation for Robust Data Representation, Pacific Rim International Conference on Artificial Intelligence, pp. 71-84, 2018.  

[40] Zhao Zhang, Lei Jia, Mingbo Zhao, Guangcan Liu, Meng Wang , Shuicheng Yan. Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification, IEEE Transactions on Big Data (T-BD), vol.5, no.2, pp.148-165, 2018. 

[39] Maojin Sun, Shijie Hao, GuangcanLiu. Semi-supervised vehicle classification via fusing affinity matrices, Signal Processing, vol. 149, pp. 118-123, 2018. 

[38] Qingshan Liu, Guangcan Liu*, Lai Li, Xiao-Tong Yuan, Meng Wang and Wei Liu, Reversed Spectral Hashing, IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), vol.29, no.6, pp. 2441-2449, 2018.

[37] Xingyu Xie, Xianglin Guo, Guangcan Liu, Jun Wang. Implicit Block Diagonal Low-Rank Representation. IEEE Transactions on Image Processing (T-IP), vol. 27, no.1, pp. 477-489, 2018. 

[36] Zhengbo Yu, Qingshan Liu, Guangcan Liu. Deeper Cascaded Peak-piloted Network for Weak Expression Recognition. The Visual Computer, vol. 34, no. 12, pp. 1691-1699, 2018.

[35] Guangcan Liu, Qingshan Liu, Xiao-Tong Yuan. A New Theory for Matrix Completion. Advances in Neural Information Processing Systems (NeruIPS),  pp. 785-794, Long Beach, LA, UAS, December 4 – December 9,  2017.

[34] Sujuan Wang, Yubao Sun, Renlong Hang, Qingshan Liu, Xiaotong Yuan, Guangcan Liu. Spatial-spectral locality constrained elastic net hypergraph for hyperspectral image clustering. International Journal of Remote Sensing, vol. 38, no.23, pp. 7374-7388, 2017. 

[33] Yubao Sun, Sujuan Wang, Qingshan Liu, Renlong Hang and Guangcan Liu, Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images, Remote Sensing, vol. 5, no.9, pp. 506 – 519, 2017. 

[32] Qingshan Liu, Jiankang Deng, Jing Yang, Guangcan Liu and Dacheng Tao. Adaptive Cascade Regression Model for Robust Face Alignment. IEEE Transactions on Image Processing (T-IP), vol. 26, no.2, pp. 797-807, 2017.

[31] Guangcan Liu, Qingshan Liu and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 39, no.1, pp.47-60, 2017.

[30] Lai Li, Guangcan Liu and Qingshan Liu. Advancing Iterative Quantization Hashing Using Isotropic Prior. MultiMedia Modeling, Miami, Florida, USA,1.4-1.6, pp. 174-184, 2016.

[29] Xiaotong Yuan, Ping Li, Tong Zhang, Qingshan Liu and Guangcan Liu. Learning Additive Exponential Family Graphical Models via ℓ2,1-norm Regularized M-Estimation, Advances in Neural Information Processing Systems (NeurIPS), Barcelona Spain, 12.5-12.10, 2016.   

[28] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), vol. 64, no. 21, pp. 5623-5633, 2016.

[27] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu and Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.

[26] Ten Li, Bing Chen, Bingbing Ni, Guangcan Liu and Shuicheng Yan. Multi-task Low-rank Affinity Pursuit for Image Segmentation and Annotation. ACM Transactions on Intelligent Systems and Technology (TIST), pp. 65:1-65:18, vol. 7, no. 4, 2016. 

[25] Xin Zhang, Fuchun Sun, Guangcan Liu and Yi Ma. Non-blind Deblurring of Structured Images with Geometric Deformation. The Visual Computer, vol. 31, no.2, pp. 115-117, 2015.

[24] Changqing Zhang, Si Liu, Huazhu Fu, Guangcan Liu and Xiaochun Cao. Low-Rank Tensor Constrained Multiview Subspace Clustering. International Conference on Computer Vision (ICCV),  Santiago, Chile, 2015. 

[23] Weipeng Zhang, Jie Shen, Guangcan Liu and Yong Yu. A Latent Clothing Attribute Approach for Human Pose Estimation. Asian Conference on Computer Vision (ACCV), pp. 146-161, Singapore, 2014.

[22] Guangcan Liu, Shiyu Chang and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.

[21] Guangcan Liu and Ping Li. Recovery of Coherent Data via Low-Rank Dictionary Pursuit,  Advances in Neural Information Processing Systems (NeurIPS), pp. 1206-1214, Montreal, Canada,2014. 

[20] Jie Shen, Guangcan Liu, Jia Chen, Yuqiang Fang, Jianbin Xie, Yong Yu and Shuicheng Yan. Unified Structured Learning for Simultaneous Human Pose Estimation and Garment Attribute Classification. IEEE Transactions on Image Processing (T-IP), vol. 23, no.11, pp.4786-4798, 2014.

[19] Xin Zhang, Fuchun Sun, Guangcan Liu and Yi Ma. Fast Low-Rank Subspace Segmentation. IEEE Transactions on Knowledge and Data Engineering (T-KDE), vol. 26, no. 5, pp. 1293-1297, 2014. 

[18] Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013. 

[17] Congyan Lang, Jiashi Feng, Guangcan Liu, Jinhui Tang, Shuicheng Yan and Jiebo Luo. Improving Bottom-Up Saliency Detection by Looking into Neighbors. IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), vol.23, no.6, pp.1016-1028, 2013.

[16] Bingkun Bao, Guangcan Liu, Richang Hong, Shuicheng Yan and Changsheng Xu. General Subspace Learning with Corrupted Training Data via Graph Embedding. IEEE Transactions on Image Processing (T-IP), vol.22, no.11, pp. 4380-4893, 2013.

[15] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012. 

[14] Guangcan Liu, Huan Xu and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.

[13] Yinqiang Zheng, Guangcan Liu, Shigeki Sugimoto, Shuicheng Yan and Masatoshi Okutomi. Practical Low-Rank Matrix Approximation under Robust L1-Norm. IEEE Conference on Pattern Recognition and Computer Vision (CVPR), pp. 1410-1417, Rhode Island, USA, 2012.

[12] Si Liu, Zheng Song, Guangcan Liu, Changsheng Xu, Hanqing Lu and Shuicheng Yan. Street-to-Shop: Cross-Scenario Clothing Retrieval via Parts Alignment and Auxiliary Set. IEEE Conference on Pattern Recognition and Computer Vision (CVPR), pp. 3330-3337, Rhode Island, USA, 2012.

[11] Bingkun Bao, Guangcan Liu, Changsheng Xu and Shuicheng Yan. Inductive Robust Principal Component Analysis. IEEE Transactions on Image Processing (T-IP), vol. 21, no. 8, pp. 3794-3800, 2012. 

[10] Xiangyang Wang, Wanggen Wan and Guangcan Liu. Multi-task low-rank and sparse matrix recovery for human motion segmentation. IEEE International Conference on Image Processing (ICIP), pp. 897-900, Orlando, USA, 2012.

[9] Congyan Lang, Guangcan Liu, Jian Yu and Shuicheng Yan. Saliency Detection by Multi-Task Sparsity Pursuit. IEEE Transactions on Image Processing (T-IP), vol.21, no.3, pp. 1327-1338, 2012.

[8] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011. 

[7] Bing Chen, Guangcan Liu, Jingdong Wang, Zhongyang Huang and Shuicheng Yan. Multi-task Low-rank Affinity Pursuit for Image Segmentation. International Conference on Computer Vision (ICCV), pp. 2439-2447, Barcelona, Spain, 2011.

[6] Guangcan Liu, Zhouchen Lin, Yong Yu and Xiaoou Tang. Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.

[5] Guangcan Liu, Zhouchen Lin and Yong Yu. Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010. 

[4] Guangcan Liu, Zhouchen Lin, Yong Yu and Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009. 

[3] Guangcan Liu, Zhouchen Lin and Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition, vol.42, no.11, pp. 2737-2743, 2009. 

[2] Guangcan Liu, Zhouchen Lin, Xiaoou Tang and Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil,  2007. 

[1] Guangcan Liu, Yong Yu and Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.

联系我们：

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