Liu M, Zhang D, Chen S, et al. Joint binary classifier learning for ecoc-based multi-class classification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(11): 2335-2341.
Tian Q, Chen S, Qiao L. Ordinal Margin Metric Learning and Its Extension for Cross-Distribution Image Data.Information Sciences, 2016, 349: 50-64.
Sun B, Chen S, Wang J, et al. A robust multi-class AdaBoost algorithm for mislabeled noisy data.Knowledge-Based Systems, 2016, 102: 87-102.
Yu L, Xie J, Chen S, et al. Generating labeled samples for hyperspectral image classification using correlation of spectral bands.Frontiers of Computer Science, 2016, 10(2): 292-301.
Zhu Y, Chen S, Tian Q. Spatial regularization in subspace learning for face recognition: implicit vs. explicit.Neurocomputing, 2016, 173: 1554-1564.
Gao N, Huang S J, Chen S. Multi-label active learning by model guided distribution matching.Frontiers of Computer Science, 2016, 10(5): 845-855.
Qin X, Tan X, Chen S. Mixed bi-subject kinship verification via multi-view multi-task learning.Neurocomputing, 2016, 214: 350-357.
Zou P C, Wang J, Chen S, et al. Margin distribution explanation on metric learning for nearest neighbor classification.Neurocomputing, 2016, 177: 168-178.
Liu D, Tan X. Max-margin non-negative matrix factorization with flexible spatial constraints based on factor analysis.Frontiers of Computer Science, 2016, 10(2): 302-316.
Liu D, Tan X. Local subspace smoothness alignment for constrained local model fitting.Neurocomputing, 2016, 214: 785-795.
Jin X, Tan X. Face alignment by robust discriminative Hough voting.Pattern Recognition, 2016, 60: 318-333.
Wang D, Tan X. Unsupervised feature learning with C-SVDDNet.Pattern Recognition, 2016, 60: 473-485.
Zu C, Jie B, Liu M, et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment.Brain imaging and behavior, 2016, 10(4): 1148-1159.
Shao W, Liu M, Zhang D. Human cell structure-driven model construction for predicting protein subcellular location from biological images.Bioinformatics 32(1): 114-121
Du J, Wang L, Jie B, et al. Network-based classification of ADHD patients using discriminative subnetwork selection and graph kernel PCA.Computerized Medical Imaging and Graphics, 2016, 52: 82-88.
Yousefnezhad M, Reihanian A, Zhang D, et al. A new selection strategy for selective cluster ensemble based on Diversity and Independency.Engineering Applications of Artificial Intelligence, 2016, 56: 260-272.
Zhang Y, Zhang J, Pan Z, et al. Multi-view dimensionality reduction via canonical random correlation analysis.Frontiers of Computer Science, 2016, 10(5): 856-869.
Zu C, Zhang D. Canonical sparse cross-view correlation analysis.Neurocomputing, 2016, 191: 263-272.
Tian Q, Chen S. Cross-heterogeneous-database age estimation with co-representation among them. Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), 2016: 1333-1338.(the Intel Best Scientific Paper Award (Track：Pattern Recognition and Machine Learning) )
Huang S J, Chen S. Transfer Learning with Active Queries from Source Domain. Proceedings of the 25th International Joint Conference on Artificial Intelligence, 2016: 1592-1598.
Cai Y, Tan X. Weakly supervised human body detection under arbitrary poses.Image Processing (ICIP), 2016 IEEE International Conference on. IEEE, 2016: 599-603.
Wang Y, Jin X, Tan X. Pornographic image recognition by strongly-supervised deep multiple instance learningImage Processing (ICIP), 2016 IEEE International Conference on. IEEE, 2016: 4418-4422.
Jie B, Liu M, Jiang X, et al. Sub-network Based Kernels for Brain Network Classification.Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2016: 622-629.
Yousefnezhad M, Zhang D. Decoding visual stimuli in human brain by using Anatomical Pattern Analysis on fMRI images.Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016
Liu M, Du J, Jie B, et al. Ordinal Patterns for Connectivity Networks in Brain Disease Diagnosis.International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2016: 1-9.
Zu C, Gao Y, Munsell B, et al. Identifying High Order Brain Connectome Biomarkers via Learning on Hypergraph.International Workshop on Machine Learning in Medical Imaging. Springer International Publishing, 2016: 1-9.
Hao X, Yan J, Yao X, et al. Diagnosis-Guided Method For Identifying Multi-Modality Neuroimaging Biomarkers Associated With Genetic Risk Factors In Alzheimer's Disease.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing. NIH Public Access, 2016, 21: 108.