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Liu, Guangcan

Biography:

He received the bachelor’s degree in mathematics and the Ph.D. degree in computer science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2004 and 2010,respectively. He was a Post-Doctoral Researcher with the National University of Singapore, Singapore, from 2011 to 2012; the University of Illinois at Urbana–Champaign, Champaign, IL, USA, from 2012 to 2013; Cornell University, Ithaca, NY, USA, from 2013 to 2014; and Rutgers University, Piscataway, NJ, USA, in 2014. He was a Professor with the School of Automation, Nanjing University of Information Science and Technology, Nanjing, from 2014 to 2021. He is currently a Professor with the School of Automation, Southeast University, Nanjing, China. His research interests include the areas of machine learning, computer vision, and signal processing. He was particularly interested in figuring out the role of low-dimensional models and optimization in these areas.

Selected Publications

• Update stopped. Please refer to https://scholar.google.com/citations?hl=en&user=PaFLL10AAAAJ&view_op=list_works&sortby=pubdate

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

• 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.

• 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.

• Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, 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.

• Guangcan Liu, Ping Li. Recovery of Coherent Data via Low-Rank Dictionary Pursuit. Advances in Nueral Information Processing Systems (NIPS). pp.1206--1214, 2014.

• 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.

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

• Guangcan Liu and Shuicheng Yan, Active Subspace: Towards Scalable Low-Rank Learning, Neural Computation, 2012.

• 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), 2012.

• Guangcan Liu and Shuicheng Yan, Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction, International Conference on Computer Vision (ICCV), 2011

• Guangcan Liu, Ju Sun and Shuicheng Yan, Closed-Form Solutions to A Category of Nuclear Norm Minimization Problems, NIPS Workshop on Low-Rank Methods fr Large-Scale Machine Learning, 2010.

• Guangcan Liu, Zhouchen Lin and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation, International Conference on Machine Learning (ICML), 2010

• Guangcan Liu, Zhouchen Lin, Yong Yu and Xiaoou Tang, Unsupervised Object Segmentation with A Hybrid Graph Model (HGM), IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI), Pages: 910-924, Volume 32 , Issue 5, 2010.

• Guangcan Liu, Zhouchen Lin and Yong Yu, Radon Representation-Based Feature Descriptor for Texture Classification, IEEE Trans. Img. Proc. (T-IP), Pages: 921-928, Volume 18, Issue 5, 2009.

• Guangcan Liu, Zhouchen Lin and Yong Yu, Multi-Output Regression on the Output Manifold, Pattern Recognition, Pages: 2737-2743, Volume 42, Issue 11, 2009.

• Guangcan Liu, Zhouchen Lin, Yong Yu and Xiaoou Tang, A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), 2007.

• Guangcan Liu, Xing Zhu and Yong Yu, A Learning-Based Term-Weighting Approach for Information Retrieval, AAAI, 2005.

Matlab codes

• solving the rank constrained RPCA problem (released on Nov 2012)

• blind deconvolution (image deblurring) . (released on Sep 2013, updated on July 2014)

  • solving the low-rank representation (LRR) problems. (released on May 2011, updated on Aug 2013)

  • motion segmentation and face clustering by LRR. (released on Aug 2011, updated on May 2015)

• motion segmentation and face recognition by Latent LRR. (released on Aug 2011)

• extracting Radon features from a given image (released on 2009)