a) Books or Edited Special Issues

  1. Zenglin Xu and Irwin King. Introduction to Semi-supervised Learning. CRC Press, 2014 (expected).
  2. Yi Fang, Zenglin Xu, Jiang Bian, and Ziad Al Bawab. International Journal of Web Science, Special Issue on Social Web Search and Mining. Inderscience, 2013.
  3. Zenglin Xu, Irwin King, and Michael R. Lyu. More Than Semi-supervised Learning: A Unified View on Learning with Labeled and Unlabeled Data. LAP LAMBERT Academic Publishing, 2010.

b) Invited Book Chapters

  1. Zenglin Xu, Mingzhen Mo, and Irwin King. Computational intelligence. In Alexandru Floares, editor, Semi-supervised Learning, pages 1-16. Nova Science Publishers, 2012.
  2. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Zhangbin Zhou. A novel discriminative naive bayesian network for classification. In A. Mittal and A. Kassim, editors, Bayesian Network Technologies: Applications and Graphical Models, pages 1-12. IDEA Group Inc., New York, 2007.

c) Refereed Journal Articles

  1. Shudong Huang, Zhao Kang, Zenglin Xu. Self-weighted Multi-View Clustering with Soft Capped Norm” , Knowledge-Based Systems.
  2. Bin Liu, Yingming Li, Zenglin Xu. Manifold regularized matrix completion for multi-label learning with ADMM[J]. Neural Networks, 2018, 101:57-67.
  3. Shudong Huang, Hongjun Wang, Tao Li, Tianrui Li, Zenglin Xu. Robust graph regularized nonnegative matrix factorization for clustering. Data Mining and Knowledge Discovery, 2018, 32(2): 483-503.
  4. Shudong Huang, Zenglin Xu, Jiancheng Lv. Adaptive Local Structure Learning for Document Co-clustering. Knowledge-Based Systems, 2018, 148: 74-84.
  5. Shudong Huang, Yazhou Ren, Zenglin Xu. Robust Multi-view Data Clustering with Multi-view Capped-Norm K-means. Neurocomputing, in press.
  6. Shudong Huang, Zhao Kang, Zenglin Xu. Self-weighted Multi-View Clustering with Soft Capped Norm. Knowledge-Based Systems, in press.
  7. Zhao Kang; Chong Peng; Qiang Cheng, Kernel-driven Similarity Learning, Neurocomputing, Elsevier, 2017, 267: 210-219.
  8. Chong Peng; Zhao Kang; Fei Xu; Yongyong Chen; Qiang Cheng, Image Projection Ridge Regression for Subspace Clustering, IEEE Signal Processing Letters (IEEE SPL), 2017, 24(7): 991-995.
  9. Chong Peng; Zhao Kang; Qiang Cheng, Integrating Feature and Graph Learning with Low-Rank Representation, Neurocomputing, 2017, 249(2): 106–116.
  10. Chong Peng; Zhao Kang; Yunhong Hu; Robust Graph Regularized Nonnegative Matrix Factorization for Clustering, Qiang Cheng, ACM Transactions on Knowledge Discovery from Data (ACM TKDD), Volume 11 Issue 3, Article No. 33, March 2017.
  11. Chong Peng; Zhao Kang; Yunhong Hu; Qiang Cheng, Nonnegative Matrix Factorization with Integrated Graph and Feature Learning, ACM Transactions on Intelligent Systems and Technology (ACM TIST), Vol. 8, No. 3, Article 42, February 2017.
  12. Ming Yang, DunRen Che, Wen Liu, Zhao Kang, Chong Peng, Mingqing Xiao, Qiang Cheng, On identifiability of 3-tensors of multilinear rank (1, Lr, Lr), Big Data and Information Analytics (BDIA), American Institute of Mathematical Sciences, Vol. 1, no. 4, October 2016.
  13. Chong Peng; Zhao Kang; Ming Yang, Qiang Cheng, Feature Selection Embedded Subspace Clustering, IEEE Signal Processing Letters (IEEE SPL) 23(7), 1018-1022, 2016.
  14. Zhao Kang, Chong Peng, and Qiang Cheng, Robust Subspace Clustering via Smoothed Rank Approximation , IEEE Signal Processing Letters (IEEE SPL) 22 (11), 2088-2092.
  15. Zhao Kang, Chong Peng, Jie Cheng and Qiang Cheng, LogDet Rank Minimization with Application to Subspace Clustering, Computational Intelligence and Neuroscience, Volume 2015 (2015).
  16. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Bayesian nonparametric models for multiway data analysis. IEEE Transactions on Pattern Recognition and Machine Intelligence, 2014.
  17. Haiqin Yang, Zenglin Xu, Jieping Ye, Irwin King, and Michael R. Lyu. Efficient sparse generalized multiple kernel learning. IEEE Transactions on Neural Networks, 22(3):433-446, 2011.
  18. Zenglin Xu, Irwin King, Michael R. Lyu, and Rong Jin. Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks, 21(7):1033-1047, 2010.
  19. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, and Michael R. Lyu. A novel kernel-based maximum a posteriori classification method. Neural Networks,22(7):977-987, 2009.
  20. Zenglin Xu, Irwin King, and Michael R. Lyu. Feature selection based on minimum error minimax probability machine. International Journal of Pattern Recognition and Artificial Intelligence, 21(8):1-14, 2007.

d) Refereed International Conference Articles

  1. Zhao Kang, Xiao Lu, Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification, IJCAI 2018.
  2. Lirong He, Haoli Bai, Structured Inference for Recurrent Hidden Semi-Markov Model,IJCAI 2018.
  3. Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu, Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition, Computer Vision and Pattern Recognition (CVPR 2018), (Accepted)
  4. Zhao Kang; Chong Peng; Qiang Cheng; Zenglin Xu, Unified Spectral Clustering with Optimal Graph, The Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Lousiana, Feb. 2018. (Accept rate 24.5%)
  5. Zhao Kang; Chong Peng; Ming Yang, Qiang Cheng, Exploiting Nonlinear Relationships for Top-N Recommender Systems, The 8th IEEE International Conference on Big Knowledge, Hefei, China, August. 2017. (Accept rate 27%)
  6. Chong Peng; Zhao Kang; Qiang Cheng, Subspace Clustering via Variance Regularized Ridge Regression, The Thirtieth IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, Hawaii, July, 2017. (Accept rate 29%)
  7. Zhao Kang; Chong Peng; Qiang Cheng, Clustering with Adaptive Manifold Structure Learning, The 33rd IEEE International Conference on Data Engineering (ICDE 2017), San Diego, USA, April. 2017. (Accept rate 28.9%)
  8. Zhao Kang; Chong Peng; Qiang Cheng, Twin Learning for Similarity and Clustering: A Unified Kernel Approach, The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, California USA, Feb. 2017. (Accept rate 24.6%)
  9. Yazhou Ren, Peng Zhao, Yongpan Sheng, Dezhong Yao, and Zenglin Xu. Robust Softmax Regression for Multi-class Classification with Self-Paced Learning, In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017, Accepted.
  10. Yazhou Ren, Peng Zhao, Zenglin Xu, and Dezhong Yao. Balanced self-paced learning with feature corruption. In Proceedings of the International Joint Con- ference on Neural Networks (IJCNN) pages 2064–2071, 2017.
  11. S Huang, Zenglin Xu, F Wang.Nonnegative matrix factorization with adaptive neighbors.Neural Networks (IJCNN), 2017 International Joint Conference on, 486-493.
  12. L Wang, Y Wang, B Liu, L He, S Liu, G de Melo,Zenglin Xu.Link prediction by exploiting network formation games in exchangeable graphs.Neural Networks (IJCNN), 2017 International Joint Conference on, 619-626.
  13. B Liu, Zenglin Xu, B Dai, H Bai, X Fang, Y Ren, S Zhe.Learning from semantically dependent multi-tasks.Neural Networks (IJCNN), 2017 International Joint Conference on, 3498-3505.
  14. Y Ren, P Zhao, Zenglin Xu, D Yao.Balanced self-paced learning with feature corruption.Neural Networks (IJCNN), 2017 International Joint Conference on, 2064-2071.
  15. G Li, Zenglin Xu, L Wang, J Ye, I King, M Lyu.Simple and efficient parallelization for probabilistic temporal tensor factorization.Neural Networks (IJCNN), 2017 International Joint Conference on, 1-8.
  16. Y Li, M Yang, Zenglin Xu, Zhongfei (Mark) Zhang.Learning with Feature Network and Label Network Simultaneously.AAAI, 2017.
  17. Chong Peng; Zhao Kang; Qiang Cheng, A Fast Factorization-based Approach to Robust Principal Component Analysis, The IEEE International Conference on Data Mining series (ICDM 2016), Barcelona, Spain, Dec. 2016. (Accept rate 19.6%)
  18. Zhao Kang; Chong Peng; Ming Yang, Qiang Cheng, Top-N Recommendation on Graphs, The 25th ACM Int. Conf. on Information and Knowledge Management (CIKM 2016), Indianapolis, United States, Oct. 2016. (Accept rate 23.2%)
  19. Chong Peng; Zhao Kang; Ming Yang, Qiang Cheng, RAP: Scalable RPCA for Low-rank Matrix Recovery, The 25th ACM Int. Conf. on Information and Knowledge Management (CIKM 2016), Indianapolis, United States, Oct. 2016. (Accept rate 23.2%)
  20. Zhao Kang and Qiang Cheng, Top-N recommendation with novel rank approximation, 2016 SIAM Int. Conf. on Data Mining (SDM 2016), Miami, FL, May. 2016. (Accept rate 26%)
  21. Zhao Kang, Chong Peng, and Qiang Cheng, Top-N Recommender System via Matrix Completion, The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, USA, Feb. 2016. (Accept rate 26%)
  22. Wang L, Wu W, Zenglin Xu, et al. BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing[C]// International Conference on Supercomputing. ACM, 2016:20.
  23. Shandian Zhe, Yuan Q,Youngja Park,Zenglin Xu, Ian Molloy.DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays.AAAI,2016.
  24. Yingming Li,Ming Yang,Zenglin Xu,Zhongfei (Mark) Zhang.Learning with Marginalized Corrupted Features and Labels Together.AAAI,2016.
  25. Li Y, Yang M, Zenglin Xu, et al. Multi-view learning with limited and noisy tagging[C]// International Joint Conference on Artificial Intelligence. AAAI Press, 2016:1718-1724.
  26. Shandian Zhe, K Zhang,P Wang,K Lee,Zenglin Xu.Distributed Flexible Nonlinear Tensor Factorization.NIPS,2016.
  27. Zhao Kang, Chong Peng, and Qiang Cheng, Robust PCA Via Nonconvex Rank Approximation, The IEEE International Conference on Data Mining series (ICDM 2015), Atlantic, NJ, USA, Nov. 2015. (Accept rate 8.4%)
  28. Zhao Kang, Chong Peng, and Qiang Cheng, Robust Subspace Clustering via Tighter Rank Approximation, The 24th ACM Int. Conf. on Information and Knowledge Management (CIKM 2015), Melbourne, Australia, Oct. 2015. (Accept rate 17.98%)
  29. Chong Peng, Zhao Kang, Huiqing Li, Qiang Cheng, Subspace clustering using log-determinant rank approximation, The 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015), Sydney, Australia, Aug. 2015. (Accept rate 19.4%)
  30. Bin Shen, Zenglin Xu and Jan P. Allebach. Kernel Tapering: a Simple and Effective Approach to Sparse Kernels for Image Processing. International Conference on Image Processing, 2014.
  31. Shandian Zhe, Zenglin Xu and Yuan (Alan) Qi. Joint association discovery and diagnosis of Alzheimer’s disease by supervised heterogeneous multiview learning. Pacific Symposium on Biocomputing, 2014.
  32. Shouyuan Chen, Irwin King, Michael R. Lyu, and Zenglin Xu. Recovering pairwise interaction tensor. Neural Information Processing Systems (NIPS), 2013.(AR: 360/1420= 25.3%, Spotlight: 52/1420 = 3.7%)
  33. Shandian Zhe, Zenglin Xu, Yuan (Alan) Qi and Peng Yu. Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease. In Proceedings of ICML Workshop on Role of Machine Learning in Transforming Healthcare, Atlanta, GA, USA, 2013.
  34. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. In nite tucker decomposition: Non-parametric bayesian models for multiway data analysis. In ICML ‘12: Proceedings of the 29th International Conference on Machine Learning, pages 1023-1030, New York, NY, USA, 2012. Omnipress. (AR: 243/890 = 27.3%)
  35. Feng Yan, Zenglin Xu, and Yuan (Alan) Qi. Sparse matrix-variate gaussian process blockmodels for network modeling. In UAI ‘11: Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, pages 745-752. AUAI Press, 2011. (AR: 96/285=33.6%)
  36. Zenglin Xu, Feng Yan, and Yuan (Alan) Qi. Sparse matrix-variate t process blockmodels. In AAAI ‘11: Proceedings of the Twenty-Fifth AAAI Conference on Arti cial Intelligence. AAAI Press, 2011. (AR: 242/975=24.8%)
  37. Zenglin Xu, Rong Jin, Shenghuo Zhu, Michael R. Lyu, and Irwin King. Smooth optimization for effective multiple kernel learning. In AAAI ‘10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press,2010. (AR: 264/982=26.9%)
  38. Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R. Lyu. Simpleand efficient multiple kernel learning by group lasso. In ICML ‘10: Proceedings of the 27th International Conference on Machine Learning, pages 1175-1182.Omnipress, 2010. (AR: 152/594=25.6%)
  39. Haiqin Yang, Zenglin Xu, Irwin King, and Michael R. Lyu. Online learning for group lasso. In ICML ‘10: Proceedings of the 27th International Conference on Machine Learning, pages 1191-1198. Omnipress, 2010. (AR: 152/594=25.6%)
  40. Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu. Robust metric learning by smooth optimization. In UAI ‘10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, pages 244-251. AUAI Press,2010. (AR: 88/260=33.8%)
  41. Zenglin Xu, Rong Jin, Michael R. Lyu, and Irwin King. Discriminative semisupervised feature selection via manifold regularization. In IJCAI ‘09: Proceedings of the 21th International Joint Conference on Arti cial Intelligence, pages 1303-1308, 2009.
  42. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu, and Zhirong Yang. Adaptive regularization for transductive support vector machine. In Y. Bengio,L. Bottou, J. Lafferty, and C. Williams, editors, Advances in Neural Information Processing Systems 22 (NIPS), pages 2125-2133. 2009. (AR: 263/1105= 23.8%,Spotlight: 87/1105 = 7.8%)
  43. Zhirong Yang, Irwin King, Zenglin Xu, and Errki Oja. Heavy-tailed symmetric stochastic neighbor embedding. In Y. Bengio, L. Bottou, J. La erty,and C. Williams, editors, Advances in Neural Information Processing Systems 22(NIPS), pages 2169-2177. 2009. (AR: 263/1105= 23.8%, Spotlight: 87/1105 =7.8%)
  44. Zenglin Xu, Rong Jin, Jieping Ye, Michael R. Lyu, and Irwin King. Non-monotonic feature selection. In ICML ‘09: Proceedings of the 26th Annual International Conference on Machine Learning, pages 1145-1152, New York, NY,USA, 2009. ACM. (160/640 = 25%)
  45. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, and Colin Campbell. Supervised self-taught learning: Actively transferring knowledge from unlabeleddata. In IJCNN ‘09: International Joint Conference on Neural Networks, pages 1272-1277. IEEE, 2009.
  46. Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for efficient multiple kernel learning. In D. Koller, D. Schuurmans, Y. Bengio,and L. Bottou, editors,Advances in Neural Information Processing Systems 21(NIPS), pages 1825-1832. 2008. (AR: 250/1022 = 24%)
  47. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, and Michael R.Lyu. Semi-supervised text categorization by active search. In CIKM ‘08: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 1517-1518, New York, NY, USA, 2008. ACM Press. (AR: 256/772= 33%)
  48. Kaizhu Huang, Zenglin Xu, Irwin King, and Michael R. Lyu. Semi-supervised learning from general unlabeled data. In ICDM ‘08: Proceedings of IEEE International Conference on Data Mining, pages 273-282, Los Alamitos, CA, USA,2008. IEEE Computer Society. (AR: 70/724 = 9%)
  49. Jianke Zhu, Steven C. Hoi, Zenglin Xu, and Michael R. Lyu. An effective approach to 3d deformable surface tracking. In ECCV ‘08: Proceedings of the 10th European Conference on Computer Vision, pages 766-779, Berlin, Heidelberg,2008. Springer-Verlag.
  50. Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, and Michael R. Lyu. Efficient convex relaxation for transductive support vector machine. In J.C. Platt,D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1641-1648. MIT Press, Cambridge, MA, 2007.(217/975 = 22%)
  51. Zenglin Xu, Jianke Zhu, Irwin King, and Michael Lyu. Kernel maximum aposteriori classification with error bound analysis. In ICONIP ‘07: Proceedings of the International Conference on Neural Information Processing, pages 841-850,2007.
  52. Zenglin Xu, Jianke Zhu, Michael R. Lyu, and Irwin King. Maximum margin based semi-supervised spectral kernel learning. In IJCNN ‘07: Proceedings of 20th International Joint Conference on Neural Network, pages 418-423, 2007.
  53. Zenglin Xu, Irwin King, and Michael R. Lyu. Web page classification with heterogeneous data fusion. In WWW ‘07: Proceedings of the 16th International Conference on World Wide Web, pages 1171-1172, New York, NY, USA, 2007. ACM Press.