These papers are made available for personal use only, subject to author's and publisher's copyright.

Journal Publications

 Fengshan Wang, Daoqiang Zhang.  A new locality-preserving canonical correlation analysis algorithm for multi-view dimensionality reduction. Neural Processing Letters. 2013, 37(2): 135-146.                    [download]

 Shan Gao, Chen Zu, Daoqiang Zhang*.  Learning mid-perpendicular hyperplane similarity from cannot-link constraints. Neurocomputing. 2013, 113: 195-203.

 Bo Cheng, Daoqiang Zhang, Songcan Chen, DanielI.Kaufer, Dinggang Shen.  Semi-supervised multimodal relevance vector regression improves cognitive performance estimation from imaging and biological biomarkers.  Neuroinformatics. 2013, 11(3): 339-353.                    [download]

 接标, 张道强.  基于网络拓扑特性的MCI分类  数据采集与处理, 2013, 28(5):602-607.                    [download]

Conference Publications

Muhammad Yousefnezhad, Daoqiang Zhang*.  Deep Hyperalignment.  In: 31st Conference on Neural Information Processing Systems (NIPS'17), Long Beach, CA, 2017. [download]

Muhammad Yousefnezhad, Daoqiang Zhang*.  Local Discriminant Hyperalignment for multi-subject fMRI data alignment.  In: 2017 AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017. [download]

Yi Ding, Shengjun Huang, Daoqiang Zhang*.  Margin Distribution Logistic Machine.  In: 2017 SIAM International Conference on Data Mining (SDM'17), Houston, Texas, 2017. [download]

Muhammad Yousefnezhad, Daoqiang Zhang*.  Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brains.  In: 2017 SIAM International Conference on Data Mining (SDM'17), Houston, Texas, 2017. [download]

Mingliang Wang, Xiaoke Hao, Jiashuang Huang, Kangcheng Wang, Xijia Xu, Daoqiang Zhang*.  Multi-level Multi-task Structured Sparse Learning for Diagnosis of Schizophrenia Disease.  In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'17), Quebec City, Canada, 2017. [download]

Liang Sun, Wei Shao, Daoqiang Zhang*.  High-order Boltzmann machine-based unsupervised feature learning for multi-atlas segmentation.  In: IEEE International Symposium on Biomedical Imaging (ISBI'17), Melbourne, Australia, 2017. [download]

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