College of Intelligence and Computing
Assistant professor
zhangchangqing@tju.edu.cn
Bulding55, School of computer science and technology Tianjin University Peiyang Park Campus: No.135Yaguan Road, Haihe Education Park,Jinnan, Tianjin
Changqing Zhang is an assistant professor working in Lab of Machine Learning and Data Mining in Tianjin University. He received his PhD from Tianjin University in 2016, and received his B.S and M.S from Sichuan University in 2005 and 2008 respectively. He has published over 40 papers more than 10 of which are in top conferences. Besides, he serves as a reviewer for multiple journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Image Processing . He focuses mainly on multi-view learning, subspace analysis,deep learning for multi-modality data, medical image analysis, and multi-label learning.
- University of North Carolina at Chapel Hill| 2017
- PhD| Tianjin University| 2016
- M.S.| Sichuan University| 2008
- B.S.| Sichuan University| 2005
- multi-label learning
- medical image analysis
- deep learning for multi-modality data
- subspace analysis
- multi-view learning
- Papers
- [1] Latent Multiview Subspace Clustering
- [2] Flexible Multi-view Dimensionality co-Reduction
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- [3] A flexible framework of adaptive method selection for image saliency detection
- [4] Low-Rank Tensor Constrained Multiview Subspace Clustering.
- [5] Output Feature Augmented Lasso
- [6] LEAF:Latent Extended Attribute Features Discovery for Visual Classification
- [7] Exclusivity-Consistency Regularized Multi-view Subspace Clustering
- [8] Saliency-aware Nonparametric Foreground Annotation based on Weakly Labeled Data.
- [9] SketchNet: Sketch Classification with Web Images
- [10] Coupled Dictionary Learning for Unsupervised Feature Selection
- [11] Diversity-induced Multiview Subspace Clustering
- [12] Constrained Multi-view Video Face Clustering
- [13] Multi-cue Augmented Face Clustering
- [14] Graph learning for multi-view clustering.
- [15] Independence Regularized Multi-label Ensemble.
- [16] Multi-label Feature Selection with Missing Labels
- [17] Shape-Preserving Object Depth Control for Stereoscopic Images
- [18] Depth-Preserving Stereo Image Retargeting Based on Pixel Fusion
- [19] Subspace Clustering guided Unsupervised Feature Selection.
- [20] Co-regularized Unsupervised Feature Selection
- [21] Salient Object Detection via Weighted Low Rank Matrix Recovery
- [22] Semi-Supervised Multi-View Multi-Label Classification based on Nonnegative Matrix Factorization.
- [23] Unsupervised Feature Selection via Manifold Regularized Self-representation
- [24] Mixed Sparsity regularized Multi-view Unsupervised Feature Selection
- [25] Unsupervised Feature Selection via Diversity-induced Self-representation
- [26] Unsupervised Dimension Reduction via Analysis Dictionary Learning
- [27] Combining Neighborhood Separable Subspaces for Classification via Sparsity Regularized Optimization
- [28] Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
- [29] Research on axial bearing capacity of rectangular concrete-filled steel tubular columns based on artificial neural networks
- [30] Multi-View Representative and Informative induced Active Learning
- [31] Set to Set Visual Tracking
- [32] Structured Saliency Fusion Based on Dempster-Shafer Theory
- [33] Human Skin Detection via Semantic Constraint
- [34] Video Face Clustering via Constrained Sparse Representation