1. Q. Zhao, G. Zhou, L. Zhang, A. Cichocki, and S. Amari.Bayesian robust tensor factorization for incomplete data. IEEE Transactions onNeural Networks and Learning Systems (TNNLS), 27(4):736-748, 2016.
2. G. Zhou, Q. Zhao,Y. Zhang, T. Adali, S. Xie, and A. Cichocki. Linked component analysis frommatrices to high order tensors: Applications to biomedical data. Proceedings of the IEEE (PIEEE),104(2):310-331, 2016
3. T. Yokota, Q. Zhao, C. Li, and A. Cichocki. SmoothPARAFAC decomposition for tensor completion. IEEE Transactions on Signal Processing (TSP), 64(20):5423-5436, 2016.
4. Y. Zhang, G. Zhou, J.Jin, Q. Zhao, X. Wang, and A. Cichocki. Sparse Bayesian classificationof EEG for brain-computer interface. IEEE Transactions on Neural Networksand Learning Systems (TNNLS), 27(11):2256-2267, 2016.
5. Q. Zhao, L. Zhang, and A. Cichocki. Bayesian CPfactorization of incomplete tensors with automatic rankdetermination. IEEETransactions on Pattern Analysis and Machine Intelligence (TPAMI),37(9):1751-1763,2015.
6. G. Zhou, A. Cichocki, Q.Zhao, and S. Xie. Efficient nonnegative tucker decompositions: Algorithmsand uniqueness. IEEE Transaction on Image Processing (TIP),24(12):4990–5003, 2015.
7. A. Cichocki, D. Mandic,C. Caiafa, A. Phan, G. Zhou, Q. Zhao,and L. De Lathauwer. Tensor decompositions for signal processing applications:From two-way to multiway component analysis.IEEE Signal Processing Magazine (SPM),32(2):145-163, 2015.
8. G. Zhou, A. Cichocki, Q. Zhao, and S. Xie. Nonnegative matrix and tensor factorizations:An algorithmic perspective. IEEE SignalProcessing Magazine (SPM),31(3):54-65, 2014.
9. Q. Zhao, C.F. Caiafa, D.P. Mandic, A. Cichocki, et al.Higher-order partial least squares (HOPLS): A generalizedmulti-linear regression method. IEEE Transactions on Pattern Analysis and MachineIntelligence (TPAMI), 35(7):1660-673, 2013
10. Q. Zhao, G. Zhou, T. Adali, L. Zhang, and A. Cichocki.Kernerlization of tensor-based models for multiway dataanalysis. IEEESignal Processing Magazine (SPM),30(4):137-48,2013.
会议论文：
11. M. Hou, Q. Zhao, B. Chaib-draa, and A.Cichocki. Common and discriminative subspacekernel-basedmultiblock tensor partial least squares regression. In Proceedings of the 30thAAAI Conference on Artificial Intelligence (AAAI), 2016.
12. C. Li, Q. Zhao, J. Li, A. Cichocki, andL. Guo. Multi-tensor completion with commonstructures.InProceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015.
13. Q. Zhao, L. Zhang, and A.Cichocki. A tensor-variate Gaussian process for classificationofmultidimensional structured data. In Proceedings of the Twenty- Seventh AAAI Conferenceon Artificial Intelligence (AAAI), 2013.
14. Q. Zhao, C. F Caiafa, D. PMandic, A. Cichocki, et al. Multilinear subspace regression:Anorthogonal tensor decomposition approach. In Advances in NeuralInformation Processing Systems 24 (NIPS), pp.1269-277, 2011.
