Selected Publications

Recent Research Highlight:

  • Yuanyuan Liu, Fanhua Shang*, Hongying Liu, Lin Kong, Licheng Jiao, and Zhouchen Lin. Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(12): 4242-4255, 2021.

  • Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

  • Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, and Zhouchen Lin, "Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications". IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40(9): 2066–2080, 2018.

  • Yuanyuan Liu, Fanhua Shang*, Weixin An, Hongying Liu, Zhouchen Lin. “Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots”. In: Proceedings of International Conference on Machine Learning (ICML), 2022.

  • Kaiwen Zhou, Fanhua Shang*, James Cheng. "A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates". In: Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, pp. 5975–5984, 2018.

  • Yuanyuan Liu, Fanhua Shang*, James Cheng, Hong Cheng, Licheng Jiao, "Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds". In: Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS/NIPS), Long Beach, CA, USA, pp. 4875–4884, 2017.

Papers In Press:

  • Yuanyuan Liu, Fanhua Shang*, Weixin An, Hongying Liu, Zhouchen Lin. “Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots”. To appear in Proceedings of International Conference on Machine Learning (ICML), 2022. (CCF A)

  • Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan. "Exploring Example Influence in Continual Learning". To appear in: Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS/NIPS), 2022. (CCF A)

  • Dong Wang, Yicheng Liu, Liangji Fang, Fanhua Shang*, Yuanyuan Liu, Hongying Liu. “Balanced Gradient Penalty Improves Deep Long-Tailed Learning.” To appear in Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), 2022. (CCF A)

  • Weixin An, Yingjie Yue, Yuanyuan Liu, Fanhua Shang*, Hongying Liu. “A Numerical DEs Perspective on Unfolded Linearized ADMM Networks for Inverse Problems.” To appear in Proceedings of the 30th ACM International Conference on Multimedia (ACM MM), 2022. (CCF A)

  • Lin Kong, Wei Sun, Fanhua Shang*, Yuanyuan Liu, Hongying Liu. “HNO: High-order Numerical Architecture for ODE-Inspired Deep Unfolding Networks”. In: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022. (CCF A, Full oral paper)

  • Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang*, Yuanyuan Liu, Linlin Yang. “Video Super Resolution Based on Deep Learning: A Comprehensive Survey”. To appear in Artificial Intelligence Review (AIR), 2022. (SCI 1, IF: 9.588)

  • Qigong Sun, Licheng Jiao, Yan Ren, Xiufang Li, Fanhua Shang, Fang Liu. “Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization.” To appear in IEEE Transactions on Cybernetics (TC), 2022. (SCI 1, IF: 19.118)

  • Yuanyuan Liu, Jiacheng Geng, Fanhua Shang*, Weixin An, Hongying Liu, Qi Zhu, Wei Feng. “Laplacian Smoothing Stochastic ADMMs with Differential Privacy Guarantees”. To appear in IEEE Transactions on Information Forensics and Security (TIFS), 2022. (SCI 1区, IF: 7.231, CCF A)

  • Fanhua Shang, Tao Xu, Yuanyuan Liu, Hongying Liu, Longjie Shen, Maoguo Gong. “Differentially Private ADMM Algorithms for Machine Learning”, IEEE Transactions on Information Forensics and Security (TIFS), 16: 4733-4745, 2021. (SCI 1, IF: 7.231, CCF A)

  • Yuanyuan Liu, Fanhua Shang*, Hongying Liu, Lin Kong, Licheng Jiao, Zhouchen Lin. "Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning". IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(12): 4242-4255, 2021. (SCI 1, IF: 24.314, CCF A)

  • Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". IEEE Transactions on Knowledge and Data Engineering (TKDE), 32(1): 188-202, 2020. (SCI 1, IF: 9.235, CCF A)

  • Yuanyuan Liu, Jiacheng Geng, Fanhua Shang*, Weixin An, Hongying Liu, Qi Zhu, Wei Feng. “Laplacian Smoothing Stochastic ADMMs with Differential Privacy Guarantees”. To appear in IEEE Transactions on Information Forensics and Security (TIFS), 2022. (SCI 1, IF: 7.231, CCF A)

  • Hua Huang, Fanhua Shang*, Yuanyuan Liu, Hongying Liu. “Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning”. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), 2021. (CCF A)

  • Hongying Liu, Ruyi Luo, Fanhua Shang*, Mantang Niu, Yuanyuan Liu. “Progressive Semantic Matching for Video-Text Retrieval”. In: Proceedings of the 29th ACM International Conference on Multimedia (ACM MM), 2021. (CCF A)

  • Yangyang Li, Lin Kong, Fanhua Shang*, Yuanyuan Liu, Hongying Liu, Zhouchen Lin. “Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding”. In: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A)

  • Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang*, Yuanyuan Liu. “Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling”. In: Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021. (CCF A)

  • Lin Kong, Wei Sun, Fanhua Shang*, Yuanyuan Liu, Hongying Liu. “Learned Interpretable Residual Extragradient ISTA for Sparse Coding”, In: Proceedings of International Conference on Machine Learning (ICML), 2021. (CCF A)

  • Fanhua Shang, Bingkun Wei, Hongying Liu, Yuanyuan Liu, Pan Zhou and Maoguo Gong. “Efficient Gradient Support Pursuit with Less Hard Thresholding for Cardinality-Constrained Learning”. To appear in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. (SCI 1, IF: 14.255)

  • Yuanyuan Liu, Jiacheng Geng, Fanhua Shang*, Hongying Liu, Qi Zhu. “Loopless Variance Reduced Stochastic ADMM forEquality Constrained Problems in IoT Applications”. To appear in IEEE Internet of Things Journal (IOT), 2021. (SCI 1, IF: 10.238)

  • Fanhua Shang, Hua Huang, Jun Fan, Hongying Liu, Yuanyuan Liu, Jianhui Liu. “Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning”. To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021. (SCI 1, IF: 9.235, CCF A)

  • Fanhua Shang, Zhihui Zhang, Tao Xu, Yuanyuan Liu, Hongying Liu. “Principal Component Analysis in the Stochastic Differential Privacy Model”. In: Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI), 2021. (Full oral, CCF B)

  • Yang Meng, Ronghua Shang, Weitong Zhang, Fanhua Shang, Licheng Jiao, Shuyuan Yang. Graph Convolutional Neural Networks with Geometric and Discrimination Information. Engineering Applications of Artificial Intelligence, 2021. (SCI 1, IF: 7.802)

  • Jianrui Chen, Yanqing Lu, Fanhua Shang, Yuyang Wang. “A fuzzy matrix factor recommendation method with forgetting function and user features”. Applied Soft Computing, 100: 106910, 2021. (SCI 1, IF: 8.263)

  • Ronghua Shang, Lujuan Wang, Fanhua Shang, Licheng Jiao, Yangyang Li. “Dual space latent representation learning for unsupervised feature selection”. Pattern Recognition (PR), 114:107873, 2021. (SCI 1, IF: 8.518)

  • Jianrui Chen, Yanqing Lu, Fanhua Shang, Tingting Zhu. A novel recommendation scheme with multifactorial weighted matrix decomposition strategies via forgetting rule. Engineering Applications of Artificial Intelligence, 101: 104191, 2021. (SCI 1, IF: 7.802)

  • Yuzhe Ma, Ran Chen, Wei Li, Fanhua Shang, Wenjian Yu, Minsik Cho, Bei Yu, “A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks”. In: Proceedings of IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR, 2019. (CCF C, Best Student Paper Award)

  • Hongying Liu, Zhongshu Wang, Fanhua Shang*, Mingyang Zhang, Maoguo Gong, Feihang Ge, Licheng Jiao. “A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images.” In: Proceedings of the Workshop of the 19th IEEE International Conference on Data Mining (ICDM), Beijing, China, 2019. (CCF B, Best Paper Award)

  • Hengmin Zhang, Feng Qian, Fanhua Shang, Wenli Du, Jianjun Qian, Jian Yang. “Global Convergence Guarantees of (A)GIST for a Family of Noncovex Sparse Learning Problems.” To appear in IEEE Transactions on Cybernetics (TC), 2020. (SCI 1, IF: 19.118)

  • Hongying Liu, Fanhua Shang*, Shuyuan Yang, Maoguo Gong, Tianwen Zhu, Licheng Jiao. “Sparse Manifold Regularized Neural Networksfor Polarimetric SAR Terrain Classification”. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 31(8): 3007-3016, 2020. (SCI 1, IF: 14.255)

  • Ronghua Shang, Kaiming Xu, Fanhua Shang, Licheng Jiao.“Sparse and low-redundant subspace learning-based dualgraph regularized robust feature selection”. Knowledge-Based Systems, 187, 2020. (SCI 1 , IF: 8.139)

  • Mohammad Nikzad, Aaron Nicolson, Yongsheng Gao, Jun Zhou, Kuldip Paliwal, Fanhua Shang*. “Deep Residual-Dense Lattice Network for Speech Enhancement.” In: Proceedings of the 34-th AAAI Conference on Artificial Intelligence (AAAI), 2020. (CCF A)

  • Yuanyuan Liu, Fanhua Shang* and Licheng Jiao. Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor. To appear in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019.

  • Fanhua Shang, BingkunWei, Hongying Liu, Yuanyuan Liu, Jiacheng Zhuo. Efficient Semi-Stochastic Gradient Support Pursuit for Sparsity-Constrained Non-convex Optimization. To appear in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI, Workshop of Data Science Meets Optimization), 2019.

  • Qigong Sun, Fanhua Shang*, Kang Yang, Xiufang Li, Yan Ren, Licheng Jiao. Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1,+1}. To appear in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019.

  • Yang Meng, Ronghua Shang, Fanhua Shang, Licheng Jiao, Shuyuan Yang, Rustam Stolkin. Semi-supervised Graph Regularized Deep Non-negative Matrix Factorization with Bi-orthogonal Constraints for Data Representation. Conditionally accepted by IEEE Transactions on Neural Networks and Learning Systems, 2019. (SCI 1, IF: 14.255)

  • Hongying Liu, Fanhua Shang*, Shuyuan Yang, Maoguo Gong, Tianwen Zhu, Licheng Jiao. Sparse Manifold Regularized Neural Networks for Polarimetric SAR Terrain Classification. Conditionally accepted by IEEE Transactions on Neural Networks and Learning Systems, 2019. (SCI 1, IF: 14.255)

  • Ronghua Shang, Kaiming Xu, Fanhua Shang, and Licheng Jiao. Sparse and Low-redundant Subspace Learning-based Dual-graph Regularized Robust Feature Selection. To appear in Knowledge-Based Systems, 2019. (SCI 1 , IF: 8.139)

  • Ronghua Shang, Yang Meng, Wenbing Wang, Fanhua Shang, and LichengJiao. Local Discriminative Based Sparse Subspace Learning for Feature Selection. To appear in Pattern Recognition, 2019. (SCI 1, IF: 8.518)

  • Kaiwen Zhou, Qinghua Ding, Fanhua Shang, James Cheng, Danli Li, Zhiquan Luo. Direct Acceleration of SAGA using Sampled Negative Momentum. To appear in Proceedings of of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

  • Fanhua Shang, Kaiwen Zhou, Hongying Liu, James Cheng, Ivor W. Tsang, Lijun Zhang, Dacheng Tao, Licheng Jiao. "VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning". To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018. [Preprint] Code (SCI 1, IF: 9.235, CCF A)

  • Fanhua Shang, Licheng Jiao, Kaiwen Zhou, James Cheng, Yan Ren, Yufei Jin, "ASVRG: Accelerated Proximal SVRG". To appear in Proceedings of Machine Learning Research, 2018. [PDF]

  • Hengmin Zhang, Jian Yang, Fanhua Shang, Chen Gong, and Zhenyu Zhang, "LRR for Subspace Segmentation via Tractable Schatten-p Norm Minimization and Factorization". To appear in IEEE Transactions on Cybernetics, 2018. (SCI 1, IF: 19.118)

Conference Papers:

  • Kaiwen Zhou, Fanhua Shang*, James Cheng. "A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates". In Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, pp. 5975–5984, 2018. * Corresponding author

  • Fanhua Shang, Yuanyuan Liu, Kaiwen Zhou, James Cheng, Kelvin Kai Wing Ng, Yuichi Yoshida, "Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization". In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1027–1036, 2018. [PDF] Code

  • Yuanyuan Liu, Fanhua Shang*, James Cheng, Hong Cheng, Licheng Jiao, "Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds". In Proceedings of the 31st Conference on Neural Information Processing Systems (NeurIPS/NIPS), Long Beach, CA, USA, pp. 4875–4884, 2017. [PDF], [Supplementary Material]

  • Yuanyuan Liu, Fanhua Shang*, and James Cheng, "Accelerated Variance Reduced Stochastic ADMM", in: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California, USA, pp. 2287–2293, 2017. * Corresponding author

  • Fan Yang, Fanhua Shang, Yuzhen Huang, James Cheng, Jinfeng Li, Yunjian Zhao, and Ruihao Zhao, "LFTF: A Framework for Efficient Tensor Analytics at Scale", in: Proceedings of the 43rd International Conference on Very Large Data Bases (VLDB), pp. 745–756, 2017.

  • Fanhua Shang, Yuanyuan Liu, and James Cheng, "Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization", in: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, pp. 620–629, 2016. (Full oral presentation) [Final PDF], [Supplementary Material]

  • Fanhua Shang, Yuanyuan Liu, and James Cheng, "Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization", in: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, Arizona, USA, pp. 2016–2022, 2016. (Full oral presentation) [Final PDF], arXiv

  • Yuanyuan Liu, Fanhua Shang*, Wei Fan, James Cheng, and Hong Cheng, "Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion", in: Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS) 27, Quebec, Canada, pp. 1763–1771, 2014. * Contributed equally to this work

  • Fanhua Shang, Yuanyuan Liu, James Cheng, and Hong Cheng, "Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization", in: Proceedings of the 14th IEEE International Conference on Data Mining (ICDM), Shenzhen, China, pp. 965–970, 2014. (Short oral presentation)

  • Fanhua Shang, Yuanyuan Liu, and James Cheng, Hong Cheng, "Robust Principal Component Analysis with Missing Data", in: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM), Shanghai, China, pp. 1149–1158, 2014. (Full oral presentation)

  • Yuanyuan Liu, Fanhua Shang*, Hong Cheng, and James Cheng, "Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds", in: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Quebec, Canada, pp. 515–524, 2014. * Contributed equally to this work

  • Fanhua Shang, Yuanyuan Liu, and James Cheng, "Generalized Higher-Order Tensor Decomposition via Parallel ADMM", in: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Quebec, Canada, pp. 1279–1285, 2014.

  • Yuanyuan Liu, Fanhua Shang*, Hong Cheng, James Cheng, and Hanghang Tong, "Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion", in: Proceedings of the 14th SIAM International Conference on Data Mining (SDM), Philadelphia, USA, pp. 866–874, 2014. * Contributed equally to this work

  • Fanhua Shang, L. C. Jiao, and Fei Wang, "Semi-Supervised Learning with Mixed Knowledge Information", in: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Beijing, China, pp. 732–740, 2012. (17% acceptance ratio, Full oral presentation)

  • Fanhua Shang, L. C. Jiao, Yuanyuan Liu, and Fei Wang, "Learning Spectral Embedding via Iterative Eigenvalue Thresholding", in: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM), Maui, USA, pp. 1507–1511, 2012. (Short oral presentation)

  • Fanhua Shang, Yuanyuan Liu, and Fei Wang, "Learning Spectral Embedding for Semi-Supervised Clustering", in: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, pp. 597–606, 2011. (11% acceptance ratio, Full oral presentation)

Journal Papers:

  • Fanhua Shang, James Cheng, Yuanyuan Liu, Zhi-Quan Luo, and Zhouchen Lin, "Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications". IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(9): 2066–2080, 2018. [PDF], [Supplementary Material] Code

  • Fanhua Shang, Yuanyuan Liu, James Cheng, and Da Yan, "Fuzzy Double Trace Norm Minimization for Recommendation Systems". IEEE Transactions on Fuzzy Systems, 26(4): 2039-2049, 2018. [PDF], [Supplementary Material] Code

  • Yuanyuan Liu, Fanhua Shang*, Wei Fan, James Cheng, and Hong Cheng, "Generalized Higher-Order Orthogonal Iteration for Tensor Learning and Decomposition", IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 12, pp. 2551–2563, 2016. * Corresponding author Code

  • Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, and Hong Cheng, "Robust Bilinear Factorization with Missing and Grossly Corrupted Observations", Information Sciences, vol. 370, pp. 53–72, 2015. Code

  • Yuanyuan Liu, Fanhua Shang*, Licheng Jiao, James Cheng, and Hong Cheng, "Trace Norm Regularized CANDECOMP/PARAFAC Decomposition with Missing Data", IEEE Transactions on Cybernetics, vol. 45, no. 11, pp. 2437–2448, 2015. * Corresponding author Code

  • Fei Yin, L. C. Jiao, Fanhua Shang, Lin Xiong, and Shasha Mao, "Double Linear Regressions for Single Labeled Image per Person Face Recognition", Pattern Recognition, vol. 47, no. 4, pp. 1547–1558, 2014.

  • Jing Chai, Hongtao Chen, Lixia Huang, and Fanhua Shang, "Maximum Margin Multiple-Instance Feature Weighting," Pattern Recognition, vol. 47, no. 6, pp. 2091–2103, 2014.

  • Fanhua Shang, L. C. Jiao, Yuanyuan Liu, and Hanghang Tong, "Semi-Supervised Learning with Nuclear Norm Regularization", Pattern Recognition, vol. 46, no. 8, pp. 2323–2336, 2013.

  • Yuanyuan Liu, L. C. Jiao, and Fanhua Shang, "A Fast Tri-Factorization Method for Low-Rank Matrix Recovery and Completion", Pattern Recognition, vol. 46, no. 1, pp. 163–173, 2013.

  • Yuanyuan Liu, L. C. Jiao, and Fanhua Shang, "An Efficient Matrix Factorization Based Low-Rank Representation for Subspace Clustering", Pattern Recognition, vol. 46, no. 1, pp. 284–292, 2013.

  • Yuanyuan Liu, L. C. Jiao, and Fanhua Shang*, "An Efficient Matrix Bi-Factorization Alternative Optimization Method for Trace Norm Minimization", Neural Networks, vol. 48, pp. 8–18, 2013. * Corresponding author

  • Fanhua Shang, L. C. Jiao, and Fei Wang, "Graph Dual Regularization Non-Negative Matrix Factorization for Co-Clustering", Pattern Recognition, vol. 45, no. 6, pp. 2237–2250, 2012. Code

  • Fanhua Shang, L. C. Jiao, Jiarong Shi, and Fei Wang, Maoguo Gong, "Fast Affinity Propagation Clustering: A Multilevel Approach", Pattern Recognition, vol. 45, no. 1, pp. 474–486, 2012. Code

  • L. C. Jiao, Fanhua Shang*, Fei Wang, and Yuanyuan Liu, "Fast Semi-Supervised Clustering with Enhanced Spectral Embedding", Pattern Recognition, vol. 45, no. 12, pp. 4358–4369, 2012. * Corresponding author

  • Fanhua Shang, L. C. Jiao, Jiarong Shi, Maoguo Gong, and R. H. Shang, "Fast Density-Weighted Low-Rank Approximation Spectral Clustering", Data Mining and Knowledge Discovery, vol. 23, no. 2, pp. 345–378, 2011.

Book Chapter:

  • Fanhua Shang, Yuanyuan Liu, James Cheng, and Hong Cheng, "Recovering Low-Rank and Sparse Matrices with Missing and Grossly Corrupted Observations", in T. Bouwmans, N. Aybat, E. Zahzah, "Handbook on Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, May, 2016.