Journal Publications | Conference Publications | Technical Report

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Journal Publications

Huang F, Chen S, Huang S. Joint Estimation of Multiple Conditional Gaussian Graphical Models. IEEE Transactions on Neural Networks and Learning Systems.(Accept) [pdf]

Wang D, Tan X. Bayesian Neighbourhood Component Analysis. IEEE Transactions on Neural Networks and Learning Systems.(Accept)

Tian Q, Chen S. Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing, 2017, 238: 286-295. [pdf]

Wang L, Chen S. Joint representation classification for collective face recognition. Pattern Recognition, 2017, 63: 182-192. [pdf]

Song G, Tan X. Hierarchical deep hashing for image retrieval. Frontiers of Computer Science, 2017, 11(2): 253-265. [pdf]

Cai Y, Tan X, Tan X. Selective Weakly Supervised Human Detection under Arbitrary Poses. Pattern Recognition, 2017, 65: 223-237. [pdf]

Shao W, Ding Y, Shen H B, et al. Deep model-based feature extraction for predicting protein subcellular localizations from bio-images. Frontiers of Computer Science, 2017, 11(2): 243-252. [pdf]

Shao W, Liu M, Xu Y Y, et al. An Organelle Correlation-Guided Feature Selection Approach for Classifying Multi-Label Subcellular Bio-images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017. [pdf]

Yousefnezhad M, Huang S J, Zhang D. WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory. IEEE Transactions on Cybernetics, 2017. [pdf]

Hao X, Li C, Du L, et al. Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease. Scientific Reports, 2017, 7. [pdf]

Jie B, Liu M, Liu J, et al. Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease. IEEE Transactions on Biomedical Engineering, 2017, 64(1): 238-249. [pdf]

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. [pdf]

Tian Q, Chen S, Qiao L. Ordinal Margin Metric Learning and Its Extension for Cross-Distribution Image Data. Information Sciences, 2016, 349: 50-64. [pdf]

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. [pdf]

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. [pdf]

Zhu Y, Chen S, Tian Q. Spatial regularization in subspace learning for face recognition: implicit vs. explicit. Neurocomputing, 2016, 173: 1554-1564. [pdf]

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. [pdf]

Qin X, Tan X, Chen S. Mixed bi-subject kinship verification via multi-view multi-task learning. Neurocomputing, 2016, 214: 350-357. [pdf]

Zou P C, Wang J, Chen S, et al. Margin distribution explanation on metric learning for nearest neighbor classification. Neurocomputing, 2016, 177: 168-178. [pdf]

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. [pdf]

Jin X, Tan X. Face alignment by robust discriminative Hough voting. Pattern Recognition, 2016, 60: 318-333. [pdf]

Wang D, Tan X. Unsupervised feature learning with C-SVDDNet. Pattern Recognition, 2016, 60: 473-485. [pdf]

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. [pdf]

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 [pdf]

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. [pdf]

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. [pdf]

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. [pdf]

Tian Q, Chen S. A novel ordinal learning strategy: Ordinal nearest-centroid projection. Knowledge-Based Systems, 2015, 88:144-153. [pdf]

Tian Q, Chen S. Cumulative attribute relation regularization learning for human age estimation. Neurocomputing, 2015. [pdf]

Huang F, Chen S. Joint Learning of Multiple Sparse Matrix Gaussian Graphical Models. IEEE transactions on neural networks and learning systems, 2015, 26(11): 2606-2620. [pdf]

Wang, Y., Chen, S., Xue, H., & Fu, Z. Semi-supervised classification learning by discrimination-aware manifold regularization. Neurocomputing, 147, 299-306. [pdf]

Tian Q, Xue H, Qiao L. Human Age Estimation by Considering both the Ordinality and Similarity of Ages. Neural Processing Letters, 2015: 1-17. [pdf]

Shi Y, Gao Y, Liao S, Zhang D, Gao Y, Shen D. Semi-Automatic Segmentation of Prostate in CT Images via Coupled Feature Representation and Spatial-Constrained Transductive Lasso. IEEE Transactions on Pattern Analysis and Machine Intelligence,2015. [pdf]

Cheng B, Liu M, Zhang D. Domain Transfer Leraning for MCI Coversion Prediction. IEEE Transaction on Biomedical Engineering,2015,62(7), 1805–1817. [pdf]

Liu M, Zhang D, Shen D. View-centralized multi-atlas classification for Alzheimer's disease diagnosis. Human brain mapping, 2015, 36(5): 1847-1865. [pdf]

Liu M, Zhang D, Adeli M, Shen D. Inherent Structure Based Multi-view Learning with Multi-template Feature Representation for Alzheimer’s Disease Diagnosis. IEEE Transactions on Biomedical Engineering ,2015. [pdf]

Jie B, Zhang D,Cheng B, Shen D. Manifold regularized multitask feature learning for multimodality disease classification. Human brain mapping, 2015, 36(2): 489-507. [pdf]

Cheng B, Liu M, Suk H, Shen D, Zhang D. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging and Behavior, 2015,913–926. [pdf]

Ye T, Zu C, Jie B, Shen D, Zhang D. Discriminative multi-task feature selection for multi-modality classification of Alzheimer’s disease. Brain imaging and behavior, 2015: 1-11. [pdf]

Zu C, Jie B, Liu M, Chen S, Shen D, Zhang D. Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment. Brain imaging and behavior, 2015: 1-12. [pdf]

Liu D, Tan X. Max-margin Non-negative Matrix Factorization with Flexible Spatial Constraints Based on Factor Analysis. Frontiers of Computer Science, (2015) 1-15. [pdf]

Sun Z, Chen S, Qiao L. A Two-Step Regularization Framework for Non-Local Means. Journal of Computer Science and Technology, 2014, 29(6): 1026-1037. [pdf]

Yang L, Chen S. Linear discriminant analysis with worst between-class separation and average within-class compactness. Frontiers of Computer Science, 2014, 8(5): 785-792. [pdf]

Song F, Tan X, Liu X, Chen S. Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognition, 2014, 47(9): 2825-2838. [pdf]

Li Y, Si J, Zhou G, Chen S. FREL: A Stable Feature Selection Algorithm. IEEE transactions on Neural Networks and Learning Systems, 26(7), 1388-1402. [pdf]

Zou P C, Wang J, Chen S, et al. Bagging-like metric learning for support vector regression. Knowledge-Based Systems, 2014, 65: 21-30. [pdf]

Yang B, Chen S. A Comparative Study: Globality versus Locality for Graph Construction in Discriminant Analysis. Journal of Applied Mathematics, 2014. [pdf]

Tian, Q, Chen, S, & Tan, X. Comparative study among three strategies of incorporating spatial structures to ordinal image regression. Neurocomputing, 2014, 136, 152-161. [pdf]

Xue H, Chen S. Discriminality-driven regularization framework for indefinite kernel machine. Neurocomputing, 2014, 133: 209-221. [pdf]

Wang L, Chen S, Wang Y. A unified algorithm for mixed l_ {2, p}-minimizations and its application in feature selection. Computational Optimization and Applications, 2014, 58(2): 409-421. [pdf]

Song F, Tan X, Chen S. Exploiting relationship between attributes for improved face verification. Computer Vision and Image Understanding, 2014, 122: 143-154. [pdf]

Sun Z, Chen S, Qiao L. A general non-local denoising model using multi-kernel-induced measures. Pattern Recognition, 2014, 47(4): 1751-1763. [pdf]

Mei Y, Zhao B, Zhou Y, Chen S. Orthogonal curved-line Gabor filter for fast fingerprint enhancement. Electronics Letters, 2014, 50(3): 175-177. [pdf]

Liu M, Miao L, Zhang D. Two-stage cost-sensitive learning for software defect prediction. IEEE Transactions on Reliability,2014, 63(2): 676-686. [pdf]

Jie B, Zhang D, Gao W, Wang Q, Wee C, Shen D. Integration of network topological and connectivity properties for neuroimaging classification. IEEE Transactions on Biomedical Engineering, 2014, 61(2): 576-589. [pdf]

Liu M, Zhang D, Chen S. Attribute relation learning for zero-shot classification. Neurocomputing, 2014, 139: 34-46. [pdf]

Liu M, Zhang D. Sparsity Score: A Novel Graph-preserving Feature Selection Method. International Journal of Pattern Recognition and Artificial Intelligence, 2014. [pdf]

Jie B, Zhang D, Wee C, Shen D. Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Human brain mapping, 2014, 35(7): 2876-2897. [pdf]

Fei F, Jie B, Zhang D. Frequent and discriminative subnetwork mining for mild cognitive impairment classification. Brain connectivity, 2014, 4(5): 347-360. [pdf]

Chen M, Tan X. Part-Based Pose Estimation with Local and Non-Local Contextual Information. IET Computer Vsion, 2014. [pdf]

Chen K, Tan X. Sparse representations based attribute learning for flower classification. Neurocomputing, 145, 416-426, 2014. [pdf]

Qian, Q., Chen, S., & Zhou, X. Multi-view classification with cross-view must-link and cannot-link side information. Knowledge-Based Systems, 2013, 54, 137-146.[pdf]

Song, F., Tan, X., Chen, S., & Zhou, Z. H. A literature survey on robust and efficient eye localization in real-life scenarios. Pattern Recognition, 2013, 46(12), 3157-3173.[pdf]

Yang, B., & Chen, S. A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing, 2013, 120, 365-379.[pdf]

Wang, Y., & Chen, S. Safety-aware semi-supervised classification. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(11), 1763-1772.[pdf]

Qiao, L., Zhang, L., & Chen, S. Dimensionality reduction with adaptive graph. Frontiers of Computer Science, 2013, 7(5), 745-753.[pdf]

Cheng, B., Zhang, D., Chen, S., Kaufer, D. I., Shen, D., & Alzheimer’s Disease Neuroimaging Initiative. Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers. Neuroinformatics, 2013, 11(3), 339-353.[pdf]

Wang, Z., Zhu, C., Gao, D., & Chen, S. Three-fold structured classifier design based on matrix pattern. Pattern Recognition, 2013, 46(6), 1532-1555.[pdf]

Qian, Q., & Chen, S. Co-metric: a metric learning algorithm for data with multiple views. Frontiers of Computer Science, 2013, 7(3), 359-369.[pdf]

Zhou, X., Chen, X., & Chen, S. Neighborhood Correlation Analysis for Semi-paired Two-View Data. Neural processing letters, 2013, 37(3), 335-354.[pdf]

Wang, Y., & Chen, S. Soft large margin clustering. Information Sciences, 2013, 232, 116-129.[pdf]

Sun, Z., & Chen, S. Analysis of non-local Euclidean medians and its improvement. IEEE Signal Processing Letters, 2013, (20), 303-306.[pdf]

Yin, X., Chen, S., & Hu, E. Regularized soft K-means for discriminant analysis. Neurocomputing, 2013, 103, 29-42.[pdf]

Wang, Z., Jie, W., Chen, S., & Gao, D. Randomprojection ensemble learning with multiple empirical kernels. Knowledge-Based Systems, 2013, 37, 388-393.[pdf]

Wang, B., Tang, J., Fan, W., Chen, S., Tan, C., & Yang, Z. Query-dependent cross-domain ranking in heterogeneous network. Knowledge and information systems, 2013, 34(1), 109-145.[pdf]

Gao S, Zu C, Zhang D. Learning mid-perpendicular hyperplane similarity from cannot-link constraints. Neurocomputing, 2013, 113: 195-203. [pdf]

Wang F, Zhang D. A new locality-preserving canonical correlation analysis algorithm for multi-view dimensionality reduction. Neural processing letters, 2013, 37(2): 135-146. [pdf]

Liu N, Zheng X, Sun H, Tan X. Two-dimensional bar code out-of-focus deblurring via the Increment Constrained Least Squares filter. Pattern Recognition Letter, 34, 124-130,2013. [pdf]

Qian Qiang, Songcan Chen, Weiling Cai, Simultaneous clustering and classification over cluster structure representation, Pattern Recognition, 45(6):2227-2236, 2012.[pdf]

Xiaohong Chen, Songcan Chen, Hui Xue, Xudong Zhou, A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data,Pattern Recognition, 2012, 45(5): 2005-2018.[pdf]

Wang, Y, Chen, S, & Xue, H . Can under-exploited structure of original-classes help ECOC-based multi-class classification?. Neurocomputing, 2012, 89, 158-167.[pdf]

Wang, Z, Xu, J, Chen, S, & Gao, D. Regularized multi-view learning machine based on response surface technique. Neurocomputing, 2012, 97, 201-213.[pdf]

Yang, X., Chen, S., & Chen, B. Plane-Gaussian artificial neural network. Neural Computing and Applications, 2012, 21(2), 305-317.[pdf]

Zhang, L., Chen, S., & Qiao, L. Graph optimization for dimensionality reduction with sparsity constraints. Pattern Recognition, 2012, 45(3), 1205-1210.[pdf]

Wang, Y., Chen, S., & Zhou, Z. H. New semi-supervised classification method based on modified cluster assumption. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(5), 689-702.[pdf]

Gu, J., & Chen, S. Manifold based canonical correlation analysis for wireless sensor network localization. Wireless Communications and Mobile Computing, 2012, 12(15), 1389-1404.[pdf]

Zhang D, Shen D. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS one, 2012. [pdf]

Zhang D, Shen D. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage,2012,59(2): 895-907. [pdf]

Yang B, Chen S, Wu X, Structurally motivated framework for discriminant analysis , Pattern Analysis and Applications,2011, 14(4): 349-367.[pdf]

Gu J,Chen S, Sun T. Localization with Incompletely paired Data in Complex Wireless Sensor Network , IEEE Trans. Wireless Communications, 2011, 10(9): 2841-2849.[pdf]

Wang Z, Chen S, Gao D, A Novel Multi-view Learning Developed from Single-view Patterns , Pattern Recognition, 2011, 44(10-11): 2395-2413.[pdf]

Wang Z, Chen S, Gao D, A Novel Multi-view Classifier based on Nyström Approximation, Expert systems with applications, 2011, 38(9): 11193-11200.[pdf]

Wang Y, Chen S, Xue H, Support Vector Machines incorporated with feature discrimination, Expert systems with applications, 2011, 38(10):12506-12513.[pdf]

Xue H, Chen S, and Yang Q, Structural Regularized Support Vector Machine: A Framework for Structural Large Margin Classifier, IEEE Trans. on Neural Networks, 2011, 22(4): 573-587.[pdf]

Chen X, Chen S, Xue H, Large Correlation Analysis, Applied Mathematics and Computation, 2011, 217(22):9041-9052.[pdf]

Zhang J, Zhang D. A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples. Pattern Recognition, 2011, 44(6): 1162-1171. [pdf]

Chen S, Zhang D. Semisupervised dimensionality reduction with pairwise constraints for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2): 369-373. [pdf]

Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Multimodal classification of Alzheimer's disease and mild cognitive impairment. Neuroimage, 2011, 55(3): 856-867. [pdf]

Gu J, Chen S, Manifold-based Canonical Correlation Analysis for Wireless Sensor Network Localization, Wireless Communication and Mobile Computing, 10:1-16 (2010)[pdf]

Xue H, Chen S, Glocalization Pursuit Support Vector Machine, Neural Computing and applications, 20(7):1043-1053 (2010)[pdf]

Ding J, Ma R, Yang J, Chen S, A tree-structured framework for purifying "complex" clusters with structural roles of individual data, Pattern Recognition, 43(11): 3753-3767 (2010) [pdf]

Yang B, Chen S, Sample-dependent Graph Construction with Application to Dimensionality Reduction, Neurocomputing, 74(1-3): 301-314 (2010) [ pdf]

Hu E, Chen S, Zhang D, Yin X, Semi-supervised kernel matrix learning by kernel propagation, IEEE Transactions on Neural Netwoks, 21(11): 1831-1841 (2010) [pdf]

Yang B, Chen S. Disguised Discrimination of Locality-based Unsupervised Dimensionality Reduction. International Journal of Pattern Recognition and Artificial Intelligence (IJPRA), 24(7): 1011-1025 (2010) [pdf]

Liu J, Chen S, Zhou Z, Tan X, Generalized Low Rank Approximations of Matrices Revisited, IEEE Transactions on Neural Netwoks, 21(4): 621-632 (2010) [pdf]

Qiao L, Zhang L, Chen S, An empirical study of two typical locality preserving linear discriminant analysis methods, NeuroComputing, 73(10-12): 1587-1594 (2010) [ pdf]

Cai W, Chen S, Zhang D, A Multi-objective Simultaneous Learning Framework for Clustering and Classification, IEEE Trans. Neural Networks, 21(2): 185-200 (2010) [pdf]

Hu E, Chen S, Yin X, Manifold Contraction for Semi-Supervised Classification, Science in China (F), 53(6), 1170-1187, 2010 [pdf]

Wang Y, Chen S, Xue H, Structure-embedded AUC-SVM, International Journal of Pattern Recognition and Artificial Intelligence, 24(5), 667-690, 2010 [pdf]

Hu E, Yin X, Wang Y, Chen S. SSPS: A Semi-Supervised Pattern Shift for Classification. Neural Processing Letters 31(3): 243-257 (2010) [pdf]

Wang Z, Chen S, Xue H, Pan Z. A Novel Regularization Learning for Single-view Patterns: Multi-view Discriminative Regularization. Neural Processing Letters 31(3): 159-175 (2010) [pdf]

Zhang L, Qiao L, Chen S. Graph-optimized Locality Preserving Projections. Pattern Recognition 43(6): 1993-2002 (2010) [pdf]

Yin X, Chen S, Hu E, Zhang D. Semi-Supervised Clustering with Metric Learning: An Adaptive Kernel Method. Pattern Recognition 43(4): 1320-1333 (2010) [pdf]

Qiao L, Chen S, Tan X. Sparsity Preserving Discriminant Analysis for Single Training Image Face Recognition. Pattern Recognition Letters 31(5): 422-429 (2010). [pdf]

Qiao L, Chen S, Tan X. Sparsity preserving projections with applications to face recognition. Pattern Recognition 43(1): 331-341 (2010) [pdf]

Sun D, Zhang D. Bagging constraint score for feature selection with pairwise constraints. Pattern Recognition, 2010, 43(6): 2106-2118. [pdf]

Peng Y, Zhang D, Zhang J. A new canonical correlation analysis algorithm with local discrimination. Neural processing letters, 2010, 31(1): 1-15. [pdf]

Huang P, Zhang D. Locality sensitive C-means clustering algorithms. Neurocomputing, 2010, 73(16-18): 2935-2943. [pdf]

Tan X and Triggs B. Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions. IEEE Transactions on Image Processing, 19(6), 1635-1650,2010. [pdf]

Xue H, Zhu Y, Chen S. Local ridge regression for face recognition. Neurocomputing 72(4-6): 1342-1346 (2009) [pdf]

Xue H, Chen S, Yang Q. Discriminatively regularized least-squares classification. Pattern Recognition 42(1): 93-104 (2009) [pdf]

Cai W, Chen S, Zhang D. A simultaneous learning framework for clustering and classification. Pattern Recognition 42(7): 1248-1259 (2009) [pdf]

Zhu Y, Liu J, Chen S. Semi-random subspace method for face recognition. Image Vision Comput. 27(9): 1358-1370 (2009) [pdf]

Yang X, Chen S, Chen B, Pan Z. Proximal support vector machine using local information. Neurocomputing 73(1-3): 357-365 (2009) [pdf]

Tan X, Chen S, Zhou Z, and Liu J. Face Recognition under Occlusions and Variant Expressions with Partial Similarity, IEEE Transactions on Information Forensics & Security, 2009, 4(2): 217-230 [pdf]

Wang Z, Chen S. Multi-view kernel machine on single-view data. Neurocomputing 72(10-12): 2444-2449 (2009) [pdf]

Sun D, Zhang D. A new discriminant principal component analysis method with partial supervision. Neural Processing Letters, 2009, 30: 103-112. [pdf]

Zhang D, Zhou Z. (2D) 2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing,2005, 69(1): 224-231. [pdf]

Tan X, Chen S, Zhou Z. Recognizing Partially Occluded, Expression Variant Faces from Single Training Image per Person with SOM-Based kNN Ensemble. IEEE Transactions on Neural Networks 16: 875-86, 2005 [pdf]

Chen S, Li D. Modified Linear Discriminant Analysis. Pattern Recognition 38: 2005 [pdf]

Chen S, Dai Q. DLS-ICBP Neural Networks with Applications in Time Series Prediction. Neural Computing & Application 14: 250-5, 2005 [pdf]

Tan K, Chen S. Adaptively weighted sub-pattern PCA for face recognition. Neurocomputing 505-11, 2005 [pdf]

Dai Q, Chen S. Chained DLS-ICBP neural networks with multiple steps time series prediction. Neural Processing Letters 21: 95-107, 2005 [pdf]

Wang M, Chen S. Enhanced FMAM Based on Empirical Kernel Map. IEEE Transactions on Neural Networks 16: 1-9, 2005 [pdf]

Chen S, Wang M. Seeking multi-thresholds directly from support vectors for image segmentation. Neuro Computing 67: 335-44, 2005 [pdf]

Chen S, Chen L. A unified SWSI-KAMs framework and performance evaluation on face recognition. Neuro Computing 54-69, 2005[pdf]

Chen S, Sun T. Class-Information-Incorporated Principal Component Analysis. Neuro Computing 216-23, 2005 [pdf]

Zhang D, Chen S, Tan K. Improving the robustness of 'online agglomerative clustering method' based on kernel-induce distance measures. Neural Processing Letters 21: 45-51, 2005 [pdf]

Zhang D, Chen S, Zhou Z. A new face recognition method based on SVD perturbation for single example image per person. Applied Mathematics and Computation 163: 895-907, 2005 [pdf]

Chen S, Zhu Y, Zhang D. Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Pattern Recognition Letters 26: 1157-67, 2005 [pdf]

Zhang D, Chen S. Fast image compression using matrix K-L transform. Neuro Computing 68: 2005 [pdf]

Zhang D, Zhou Z. (2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition. Neuro Computing 69: 224-31, 2005 [pdf]

Conference Publications

Jing X Y, Wu F, Dong X, Shan S, Chen S. Semi-Supervised Multi-View Correlation Feature Learning with Application to Webpage Classification. Thirty-First AAAI Conference on Artificial Intelligence. 2017. [pdf]

Huang S, Gao N, Chen S. Multi-instance multi-label active learning. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 2017.

Huang S, Chen J, Mu X and Zhou Z. Cost-Effective Active Learning from Diverse Labelers. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), 2017.

Ding Y, Huang S, Zu C, Zhang D. Margin Distribution Logistic Machine. Proceedings of the 17th SIAM International Conference on Data Mining (SDM'17), 2017.

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) ) [pdf]

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. [pdf]

Cai Y, Tan X. Weakly supervised human body detection under arbitrary poses. Image Processing (ICIP), 2016 IEEE International Conference on. IEEE, 2016: 599-603. [pdf]

Wang Y, Jin X, Tan X. Pornographic image recognition by strongly-supervised deep multiple instance learning Image Processing (ICIP), 2016 IEEE International Conference on. IEEE, 2016: 4418-4422. [pdf]

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. [pdf]

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 [pdf]

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. [pdf]

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. [pdf]

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. [pdf]

Huang S, Chen S, Zhou Z. Multi-label active learning: query type matters. The 24th International Joint Conference on Artificial Intelligence (IJCAI'15), 2015. [pdf]

Fei F , Jie B, Wang L, Zhang D. Discriminative subnetwork mining for multiple thresholded connectivity-networks-based classification of mild cognitive impairment. PRNI 2014,Tubingen, Germany, June 4-6,2014. [pdf]

Hao X,Yu J, Zhang D. Identifying Genetic Associations with MRI-derived Measures via Tree-Guided Sparse Learning. MICCAI 2014, Boston, USA, September 14-18, 2014. [pdf]

Jie B, Shen D, Zhang D. Brain connectivity hyper-network for MCI classification. MICCAI 2014, Boston, USA, September 14-18, 2014. [pdf]

Wang L, Fei F , Jie B, Zhang D. Combining multiple network features for mild cognitive impairment classification. ICDMW 2014, December 14-17, Shenzhen, China. [pdf]

Lu J, Hu J, Liong V, et al. The FG 2015 Kinship Verification in the Wild Evaluation. Proceedings of the 10th International Conference on Automatic Face and Gesture Recognition (FG'15), 2015. [pdf]

Fei F , Jie B, Wang L, Zhang D. Discriminative subnetwork mining for multiple thresholded connectivity-networks-based classification of mild cognitive impairment. PRNI 2014,Tubingen, Germany, June 4-6,2014. [pdf]

Hao X,Yu J, Zhang D. Identifying Genetic Associations with MRI-derived Measures via Tree-Guided Sparse Learning. MICCAI 2014, Boston, USA, September 14-18, 2014. [pdf]

Jie B, Shen D, Zhang D. Brain connectivity hyper-network for MCI classification. MICCAI 2014, Boston, USA, September 14-18, 2014. [pdf]

Wang L, Fei F , Jie B, Zhang D. Combining multiple network features for mild cognitive impairment classification. ICDMW 2014, December 14-17, Shenzhen, China. [pdf]

Zhang P, Tan X, Jin X. Action Recognition from a Single Web Image Based on an Ensemble of Pose Experts. Proceedings of the 12th Asian Conference on Computer Vision (ACCV 2014), Singapore, 2014. [pdf]

Song F, Tan X. Learning One-Shot Exemplar SVM from the Web for Face Verification. Proceedings of the 12th Asian Conference on Computer Vision (ACCV 2014), Singapore, 2014. [pdf]

Wang D, Tan X. Robust Distance Metric Learning in the Presence of Label Noise. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Canada, 2014 . [pdf]

Wang D, Tan X. Label-denoising Auto-Encoder for Classification with Inaccurate Supervision Information. Proceedings of the 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden, 2014. [pdf]

Li Y, Huang S, Chen S, & Si J. Stable L2-Regularized ensemble feature weighting. In Multiple Classifier Systems 2013 (pp. 167-178). Springer Berlin Heidelberg. [pdf]

Liu M, Chen S, & Zhang D. Learning attribute relation in attribute-based zero-shot classification. In Intelligent Science and Intelligent Data Engineering 2013, (pp. 514-521). Springer Berlin Heidelberg. [pdf]

Hao X, Zhang D. Ensemble Universum SVM Learning for Multimodal Classification of Alzheimer's Disease. MLMI 2013, Nagoya, Japan, September 22, 2013. [pdf]

Cheng B, Zhang D, Jie B, Shen D. Sparse Multimodal Manifold-Regularized Transfer Learning for MCI Conversion Prediction. MLMI 2013, Nagoya, Japan, September 22, 2013. [pdf]

Jie B, Zhang D, Suk H,Wee C,Shen D. Integrating Multiple Network Properties for MCI Identification. MLMI 2013, Nagoya, Japan, September 22, 2013. [pdf]

Jie B,Zhang D, Cheng B, Shen D. Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer’s disease. MLMI 2013, Nagoya, Japan, September 22, 2013. [pdf]

Wang D, Tan X. Centering SVDD for Unsupervised Feature Representation in Object Classification. Proceedings of the 2013 International Conference on Neural Information Processing (ICONIP 2013), Daegu, Korea, 2013. [pdf]

Jin X, Tan X, Zhou J. Face Alignment Using Local Hough Voting. Proceedings of the 10th International Conference on Automatic Face and Gesture Recognition (FG 2013), Shanghai, China, 2013. [pdf]

Li Y, Gao S, Chen S. Ensemble FeatureWeighting Based on Local Learning and Diversity.AAAI 2012. [pdf]

Song F, Tan X, & Chen S. Exploiting relationship between attributes for improved face verification. Computer Vision and Image Understanding, 2012, 122, 143-154. [pdf]

Xue H, Chen S, & Huang J. Discriminative indefinite kernel classifier from pairwise constraints and unlabeled data. In International Conference on Pattern Recognition (ICPR) 2012 (pp. 497-500). [pdf]

Liu X, Tan X, & Chen S. Eyes closeness detection using appearance based methods. In Intelligent Information Processing VI 2012, (pp. 398-408). Springer Berlin Heidelberg. [pdf]

Liu M, Chen S, & Zhang D . Learning attribute relation in attribute-based zero-shot classification. In Intelligent Science and Intelligent Data Engineering 2012, (pp. 514-521). Springer Berlin Heidelberg. [pdf]

Zhang D, Liu J, Shen D. Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis. MICCAI 2012, Nice, France, Oct. 1-5, 2012. [pdf]

Cheng B, Zhang D, Shen D. Domain Transfer Learning for MCI Conversion Prediction. MICCAI 2012, Nice, France, Oct. 1-5, 2012. [pdf]

Jie B, Zhang D, Wee C, Shen D. Structural Feature Selection for Connectivity Network-Based MCI Diagnosis. MBIA 2012, Nice, France, Oct. 1, 2012. [pdf]

Liu M, Sun D, Zhang D. Sparsity Score: A New Filter Feature Selection Method Based on L1 Graph. ICPR 2012, Tsukuba, Japan, Nov. 11-15, 2012. [pdf]

Miao L, Liu M, Zhang D. Cost-Sensitive Feature Selection with Application in Software Defect Prediction. ICPR 2012, Tsukuba, Japan, Nov. 11-15, 2012. [pdf]

Guo Q, Zhang D. Semi-Supervised Sparse Label Fusion for Multi-atlas Based Segmentation. CCPR 2012, Beijing, China, September 24-26, 2012. [pdf]

Zhu L, Miao L, Zhang D. Iterative Laplacian score for feature selection. CCPR 2012, Beijing, China, Sep. 24-26, 2012. [pdf]

Zu C, Zhang D. Sparsity preserving canonical correlation analysis. CCPR 2012, Beijing, China, Sep. 24-26, 2012. [pdf]

Hu E, Wang B, Chen S, BCDNPKL : Scalable Non-Parametric Kernel Learning Using Block Coordinate Descent , ICML 2011 [pdf]

Zhang D, Shen D. MultiCost: Multi-stage Cost-sensitive Classification of Alzheimer’s Disease. MLMI 2011, Toronto, Canada, Sep. 18, 2011. [pdf]

Cheng B, Zhang D, Chen S, Shen D. Predicting Clinical Scores Using Semi-supervised Multimodal Relevance Vector Regression. MLMI 2011, Toronto, Canada, Sep. 18, 2011. [pdf]

Zhang D, Wu G, Jia H, Shen D. Confidence-Guided Sequential Label Fusion for Multi-Atlas Based Segmentation. MICCAI 2011, Toronto, Canada, Sep. 18-22, 2011. [pdf]

Zhang D,Shen D. Multi-Modal Multi-Task Learning for Joint Prediction of Clinical Scores in Alzheimer's Disease. MBIA 2011,Toronto, Canada, September 18,2011. [pdf]

Zhang D,Shen D. Semi-supervised multimodal classification of Alzheimer's disease. ISBI 2011, Chicage, USA, March 2- April 2, 2011. [pdf]

Tan X, Li Y, Liu J, Jiang L. Face Liveness Detection from A Single Image with Sparse Low Rank Bilinear Discriminative Model. Proceedings of the 11th European Conference on Computer Vision (ECCV'10), Crete, Greece. September 2010. [pdf]

Xiang S, Chen S, Qiao L. Sparse Representation: Extract Adaptive Neighborhood for Multilabel Classification. PRICAI 2010: 304-314. [pdf]

Zhang J, Zhang D. Canonical random correlation analysis. ACM 2010, Barelona, Spain. September 26-30, 2010. [pdf]

Wang B, Tang J, Fan W, Chen S, Yang Z, and Liu Y, Heterogeneous Cross Domain Ranking in Latent Space, accepted by CIKM 2009 (full paper, acceptance rate:14.5%) [pdf]

Zhang D, Liu W. An Efficient Nonnegative Matrix Factorization Approach in Flexible Kernel Space , International Joint Conference on Artificial Intelligence, Pasadena, California, 2009. 

Liu J, Chen J, Chen S, and Ye J. Learning the Optimal Neighborhood Kernel for Classification, International Joint Conference on Artificial Intelligence, Pasadena, California, 2009. 

Tan X, Song S, Zhou Z and Chen S. Enhanced Pictorial Structures for Precise Eye Localization under Uncontrolled Conditions,  IEEE Computer Society Conference on Computer Vision and Pattern Recognition , Miami, Florida, USA, June 2009.

Tan X, L.Qiao, W.Gao and J.Liu. Robust Faces Manifold Modeling: Most Expressive Vs. Most Sparse Criterion. Subspace 2009 Workshop in conjunction with ICCV2009, Kyoto, Japan. [pdf]

Sun T,Chen S, Yang J, Shi P. A Supervised Combined Feature Extraction Method for Recognition(short paper). ICDM,2008 [pdf]

Zhang D, Chen S, Zhou Z, Yang Q. Constraint Projections for Ensemble Learning. AAAI, 2008[pdf]

Ding J, Ma R, Chen S, Yang J. Clustering Using Normalized Path-Based Metric. ISNN 2008[pdf]

Xue H, Chen S, Yang Q. Structural Support Vector Machine. ISNN 2008 [pdf]

Technical Report

Xue H, Chen SC, Yang Q: Discriminative regularization: A new classifier learning method. Technical Report 2007[pdf]

顾晶晶,陈松灿,庄毅: 利用局部保持的典型相关分析定位无线传感器网络节点. Technical Report 2010[pdf]

杨磊磊,陈松 灿: Supplements to WSAC. Technical Report 2013[pdf]