Topic: Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm   (2019/9/29)

Reporter: Xiang-Fei Yang

Reference: Lu, Canyi, et al. "Tensor robust principal component analysis with a new tensor nuclear norm." IEEE transactions on pattern analysis and machine intelligence (2019).

Topic: A Simple and Fast Algorithm for L1-norm Kernel PCA   (2019/9/22)

Reporter: Chun-Na Li

Reference: Kim, Cheolmin, and Diego Klabjan. "A Simple and Fast Algorithm for L1-norm Kernel PCA." IEEE transactions on pattern analysis and machine intelligence (2019).

Topic: A Divide-and-Conquer Solver for Kernel Support Vector Machines   (2019/9/15)

Reporter: Jun Zhang

Reference: Hsieh, Cho-Jui, Si Si, and Inderjit Dhillon. "A divide-and-conquer solver for kernel support vector machines." International conference on machine learning. 2014.

Topic: Membership Affinity Lasso for Fuzzy Clustering   (2019/9/8)

Reporter: Yan-Ru Guo

Reference: Guo, Li, et al. "Membership Affinity Lasso for Fuzzy Clustering." IEEE Transactions on Fuzzy Systems (2019).

Topic: Absent Multiple Kernel Learning Algorithms   (2019/9/1)

Reporter: Yu-Ting Zhao

Reference: Liu, Xinwang, et al. "Absent multiple kernel learning." Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.

Topic: Learning SVM Classifiers with Indefinite Kernels   (2019/7/7)

Reporter: Ling-Wei Huang

Reference: Gu, Suicheng, and Yuhong Guo. "Learning SVM classifiers with indefinite kernels." Twenty-Sixth AAAI Conference on Artificial Intelligence. 2012.

Topic: Classification With Truncated Distance Kernel   (2019/6/16)

Reporter: Yuan-Hai Shao

Reference: Huang, Xiaolin, et al. "Classification With Truncated $\ell _ {1} $ Distance Kernel." IEEE transactions on neural networks and learning systems 29.5 (2017): 2025-2030.

Topic: Intuitionistic Fuzzy Twin Support Vector Machines   (2019/6/9)

Reporter: Zhen Wang

Reference: Rezvani, Salim, Xizhao Wang, and Farhad Pourpanah. "Intuitionistic Fuzzy Twin Support Vector Machines." IEEE Transactions on Fuzzy Systems (2019).

Topic: Clustervision: Visual supervision of unsupervised clustering   (2019/5/26)

Reporter: Zhen Wang

Reference: Kwon B C, Eysenbach B, Verma J, et al. Clustervision: Visual supervision of unsupervised clustering[J]. IEEE transactions on visualization and computer graphics, 2017, 24(1): 142-151.

Topic: Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization   (2019/5/19)

Reporter: Xin-Xin Duan

Reference: Tsuchiya T, Charoenphakdee N, Sato I, et al. Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization[J]. arXiv preprint arXiv:1901.11351, 2019.

Topic: LSCCA-Canonical Correlation Analysis for Multilabel Classification_ A Least-Squares Formulation, Extensions, and Analysis   (2019/5/12)

Reporter: Ming-Zeng Liu

Reference: Sun L , Ji S , Ye J . Canonical Correlation Analysis for Multilabel Classification: A Least-Squares Formulation, Extensions, and Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(1):194-200.

Topic: Dimensionality reduction in multiple ordinal regression   (2019/5/5)

Reporter: Yong-Gang Liu

Reference: Zeng J, Liu Y, Leng B, et al. Dimensionality reduction in multiple ordinal regression[J]. IEEE transactions on neural networks and learning systems, 2017, 29(9): 4088-4101.

Topic: A general model for plane-based clustering with   (2019/4/28)

Reporter: Yu-Ting Zhao

Reference: Wang Z, Shao Y H, Bai L, et al. A general model for plane-based clustering with loss function[J]. arXiv preprint arXiv:1901.09178, 2019.

Topic: Absent Multiple Kernel Learning Algorithms   (2019/4/14)

Reporter: Jun Zhang

Reference: Liu X , Wang L , Yin J , et al. Absent Multiple Kernel Learning[C]// AAAI2015. IEEE, 2015.

Topic: Multi-task proximal support vector machine   (2019/4/7)

Reporter: Wei-Jie Chen

Reference: Li Y, Tian X, Song M, et al. Multi-task proximal support vector machine[J]. Pattern Recognition, 2015, 48(10): 3249-3257.

Topic: Kernel Methods for Deep Learning   (2019/3/24)

Reporter: Yuan-Hai Shao

Reference: Cho Y, Saul L K. Kernel methods for deep learning[C]//Advances in neural information processing systems. 2009: 342-350.

Topic: Support Vector Machine Learning for Interdependent and Structured Output Spaces   (2019/3/17)

Reporter: Chun-Na Li

Reference: Tsochantaridis I , Hofmann T , Joachims T , et al. Support Vector Machine Learning for Interdependent and Structured Output Spaces[J]. Machine Learning, 2004.

Topic: Fast Cross-Validation for Kernel-based Algorithms   (2019/3/3)

Reporter: Ling-Wei Huang

Reference: Liu Y, Liao S, Jiang S, et al. Fast Cross-Validation for Kernel-based Algorithms[J]. IEEE transactions on pattern analysis and machine intelligence, 2019.

Topic: DC programming and DCA for sparse Fisher linear discriminant analysis   (2019/1/6)

Reporter: Chun-Na Li

Reference: Le Thi H A, Phan D N. DC programming and DCA for sparse Fisher linear discriminant analysis[J]. Neural Computing and Applications, 2017, 28(9): 2809-2822.

Topic: Face recognition using discriminant locality preserving projections based on maximum margin criterion   (2018/12/23)

Reporter: Kai-Li Yang

Reference: Lu G F, Lin Z, Jin Z. Face recognition using discriminant locality preserving projections based on maximum margin criterion.[J]. Pattern Recognition, 2010, 43(10):3572-3579.

Topic: Indefinite kernels in least squares support vector machines and principal component analysis   (2018/12/16)

Reporter: Jun Zhang

Reference: Huang X , Maier A , Hornegger J , et al. Indefinite kernels in least squares support vector machines and principal component analysis[J]. Applied and Computational Harmonic Analysis, 2016:S1063520316300495.

Topic: From Convex to Nonconvex: A Loss Function Analysis for Binary Classification   (2018/12/9)

Reporter: Ling-Wei Huang

Reference: Zhao L, Mammadov M, Yearwood J. From Convex to Nonconvex: A Loss Function Analysis for Binary Classification[C]// IEEE International Conference on Data Mining Workshops. 2010.

Topic: A refined convergence analysis of pDCAe with applications to simultaneous sparse recovery and outlier detection   (2018/12/2)

Reporter: Chun-Na Li

Reference: Liu T, Pong T K, Takeda A. A refined convergence analysis of pDCA$_e$ with applications to simultaneous sparse recovery and outlier detection[J]. 2018.

Topic: Clustering with Partition Level Side Information   (2018/11/25)

Reporter: Zhen Wang

Reference: Liu H, Fu Y. Clustering with partition level side information[C]//Data Mining (ICDM), 2015 IEEE International Conference on. IEEE, 2015: 877-882.

Topic: The GEPSVM Classifier Based on L1-Norm Distance Metric   (2018/11/11)

Reporter: Kai-Li Yang

Reference: Yan A H , Ye B Q , C. Ying’an Liu, et al. The GEPSVM Classifier Based on L1-Norm Distance Metric[M]// Pattern Recognition. Springer Singapore, 2016.

Topic: A multi-kernel framework with nonparallel support vector machine   (2018/11/4)

Reporter: Jun Zhang

Reference: Tang J, Tian Y. A multi-kernel framework with nonparallel support vector machine[J]. Neurocomputing, 2017, 266.

Topic: Deep Restricted Kernel Machines Using Conjugate Feature Duality   (2018/10/28)

Reporter: Yuan-Hai Shao

Reference: Suykens J A K. Deep Restricted Kernel Machines Using Conjugate Feature Duality[J]. Neural Computation, 2017, 29(8):1-41.

Topic: Supervised tensor learning   (2018/10/21)

Reporter: Ya-Fen Ye

Reference: Tao D, Li X, Wu X, et al. Supervised tensor learning[J]. Knowledge and Information Systems, 2007, 13(1):1-42.

Topic: Dimensionality reduction in multiple ordinal regression   (2018/9/30)

Reporter: Ming-Zeng Liu

Reference: Zeng J, Liu Y, Leng B, et al. Dimensionality Reduction in Multiple Ordinal Regression.[J]. IEEE Trans Neural Netw Learn Syst, 2018, 29(9):4088-4101.

Topic: Least squares twin bounded support vector machines based on L1-norm distance metric for classification   (2018/9/23)

Reporter: Wei-Jie Chen

Reference: Yan H, Ye Q, Zhang T, et al. Least squares twin bounded support vector machines based on L1-norm distance metric for classification[J]. Pattern Recognition, 2017, 74.

Topic: New Approaches to Support Vector Ordinal Regression   (2018/9/16)

Reporter: Yong-Gang Liu

Reference: Wei C, Keerthi S S. New approaches to support vector ordinal regression[C]// International Conference on Machine Learning. ACM, 2005:145-152.

Topic: Non-parallel support vector classifiers with different loss functions   (2018/9/4)

Reporter: Ling-Wei Huang

Reference: Mehrkanoon S, Huang X, Suykens J A K. Non-parallel support vector classifiers with different loss functions[M]. Elsevier Science Publishers B. V. 2014.

Topic: Multiview Privileged Support Vector Machines   (2017/10/17)

Reporter: Yuan-Hai Shao

Reference: Tang, J., Tian, Y., Zhang, P., & Liu, X. (2017). Multiview Privileged Support Vector Machines. IEEE Transactions on Neural Networks and Learning Systems.

Topic: Robust clustering by detecting density peaks   (2017/5/2)

Reporter: Kai-Li Yang

Reference: Juanying Xie,∗，Hongchao Gao，Weixin Xie，Xiaohui Liu，Philip W. Grant J. Xie et al. Information Sciences 354 (2016) 19–40

Topic: 1-NRTSVM via ADMM   (2017/3/28)

Reporter: Chen Wang

Reference: XU Haitao，L Fan. 1-NRTSVM via ADMM for Automatical Feature Selection and Classification Simultaneously, WSEAS Transactions on Computers. 56(14):133-142.

Topic: ADMM for NPSVM   (2017/3/21)

Reporter: Chen Wang

Reference: Shen X, Niu L, Tian Y, et al. Alternating Direction Method of Multipliers for Nonparallel Support Vector Machines, IEEE International Conference on Data Mining Workshop. IEEE, 2015:1171-1176.

Topic: Sparse Subspace Clustering   (2016/12/31)

Reporter: Zhen Wang

Reference: Elhamifar E, Vidal R. Sparse subspace clustering. Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE Xplore, 2009:2790-2797.

Topic: Large-scale Binary Quadratic Optimization   (2016/12/10)

Reporter: Jiang-Rong Chen

Reference: Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications. IEEE Transactions on Pattern Analysis and Machine Intelligence. (In Press)

Topic: Representative Vector Machines   (2016/11/26)

Reporter: Yuan-Hai Shao

Reference: Representative vector machines: a unified framework for classical classifiers. IEEE Transactions on Cybernetics, 46(8): 1877-1888,2016.

Topic: Optimal Margin Distribution Machine   (2016/11/12)

Reporter: Ming-Zeng Liu

Reference: optimal margin distribution machine, Artificial Intelligence Journal (submitted)

Topic: Support Vector Machine Classifier with Pinball Loss   (2016/10/29)

Reporter: Chen Wang

Reference: Huang, X., Shi, L., & Johan A.K. Suykens. Support Vector Machine Classifier with Pinball Loss , IEEE Transactions on Pattern Analysis and Machine Intelligence,2014

Topic: Unconstrained Lagrangian TWSVM   (2016/10/15)

Reporter: Wei-Jie Chen

Reference: S Balasundaram, D Gupta, S Prasad. A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization[J]. Applied Intelligence. 2016: 1-11.(Inpress)

Topic: Multiple Graph Learning   (2016/9/28)

Reporter: Hua-Xing Pei

Reference: Nie, F., Li, J., & Li, X. Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification.IJCAI,2016

Topic: Extensions of Kmeans   (2016/9/14)

Reporter: Zhen Wang

Reference: Xiaohui Huang, et. al. Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 25, NO. 8, AUGUST 2014

Topic: Kernel Methods for Deep Learning   (2013/3/24)

Reporter: Yuan-Hai Shao

Reference: Cho Y, Saul L K. Kernel methods for deep learning[C]//Advances in neural information processing systems. 2009: 342-350.

Topic: Kernel Methods for Deep Learning   (2013/3/24)

Reporter: Yuan-Hai Shao

Reference: Cho Y, Saul L K. Kernel methods for deep learning[C]//Advances in neural information processing systems. 2009: 342-350.

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