教授
Supervisor of Master's Candidates
Date of Employment:2006-07-01
School/Department:自动化学院
Gender:Male
Contact Information:yiming@gdut.edu.cn
Degree:Doctor of Engineering
Status:调出
Discipline:模式识别与智能系统
The Last Update Time: ..
[1] Ming Yin,Junbin Gao,Zhouchen Lin,Laplacian Regularized Low-Rank Representation and Its Applications.IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,(2016, 38(3)):504-517.
[2] Lijuan Wang,Jiawen Huang,Ming Yin*,Ruichu Cai,Zhifeng Hao,Block diagonal representation learning for robust subspace clustering..Information Sciences,2020,(2020, 526 (7)):54–67.
[3] Ming Yin,Weitian Huang,Junbin Gao,Shared Generative Latent Representation Learning for Multi-view Clustering.AAAI Conference on Artificial Intelligence (AAAI-20),2020,2020. (CCF A类会议).
[4] Ming Yin,Junbin Gao,Shengli Xie,Yi Guo,Multiview Subspace Clustering via Tensorial t-Product Representation.IEEE Transactions on Neural Networks and Learning Systems,2019,(2019, 30(3)):851-864.
[5] Ming Yin,Shengli Xie,Zongze Wu,Yun Zhang,Junbin Gao,Subspace Clustering via Learning an Adaptive Low-rank Graph.IEEE Transactions on Image Processing (TIP),2018,(2018, 27(8)):3716-3728.
[6] Ming Yin,Junbin Gao,Zhouchen Lin,Qinfeng Shi,Yi Guo,Dual Graph Regularized Latent Low-rank Representation for Subspace Clustering.IEEE Transactions on Image Processing (TIP),2015,(2015, 24(12)):4918-4933.
[7] Yi Guo,Junbin Gao,Stephen Tierney,Feng Li,Ming Yin,Low Rank Sequential Subspace Clustering.The annual International Joint Conference on Neural Networks(IJCNN) 2015,2015,p:1-8. (CCF C类会议).
[8] Ming Yin,Junbin Gao,Shuting Cai,Image Super-resolution via 2D Tensor Regression Learning.Computer Vision and Image Understanding,2015,(2015, 132):12-23.
[9] Junbin Gao,Yi Guo,Ming Yin,Restricted Boltzmann Machine Approach to Couple Dictionary Training for Image Super-Resolution.IEEE International Conference on Image Processing (ICIP),2013,2013, p: 499-503. (CCF C类会议).
[10] Ming Yin,Shuting Cai,Junbin Gao,Robust Face Recognition via Double Low-Rank Matrix Recovery for feature extraction.IEEE International Conference on Image Processing (ICIP),2013,2013, p: 3770-3774. (CCF C类会议).