RIKEN Center for Advanced Intelligence Project Tensor Learning Team
Team Leader: Qibin Zhao (D.Eng.)
We study various tensor-based machine learning technologies, e.g., tensor decomposition, multilinear latent variable model, tensor regression and classification, tensor networks, deep tensor learning, and Bayesian tensor learning, with aim to facilitate the learning from high-order structured data or large-scale latent space. Our goal is to develop innovative, scalable and efficient tensor learning algorithms supported by theoretical principles. The novel applications in computer vision and brain data analysis will also be explored to provide new insights into tensor learning methods.
Main Research Fields
- Computer Science
Related Research Fields
- Neuroscience & Behavior
- Tensor Decomposition and Tensor Networks
- Bayesian Tensor Learning
- Deep Tensor Learning
- 1.G. Zhou, Q. Zhao, Y. Zhang, T. Adali, S. Xie, and A. Cichocki:
Linked component analysis from matrices to high order tensors: Applications to biomedical data"
Proceedings of the IEEE (PIEE), 104(2):310-331, (2016).
- 2.Q. Zhao, G. Zhou, L. Zhang, A. Cichocki, and S. Amari:
"Bayesian robust tensor factorization for incomplete multiway data"
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 27(4):736-748, (2016).
- 3.H. Ming, Q. Zhao, B. Chaib-draa, and A. Cichocki:
"Common and discriminative subspace kernel-based multiblock tensor partial least squares regression"
Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI’16), pp. 1673-1679, (2016).
- 4.G. Zhou, A. Cichocki, Q. Zhao, and S. Xie:
"Efficient nonnegative Tucker decompositions: Algorithms and uniqueness"
IEEE Transaction on Image Processing (TIP), 24(12):4990-5003, (2015).
- 5.Q. Zhao, L. Zhang, and A. Cichocki:
"Bayesian CP factorization of incomplete tensors with automatic rank determination"
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 37(9):1751-1763, (2015).
- 6.C. Li, Q. Zhao, J. Li, A. Cichocki, and L. Guo:
"Multi-tensor completion with common structures"
Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), pp. 2743-2749, (2015).
- 7.Q. Zhao, G. Zhou, T. Adali, L. Zhang, and A. Cichocki:
"Kernerlization of tensor-based models for multiway data analysis"
IEEE Signal Processing Magazine (SPM), 30(4):137-148, (2013).
- 8.Q. Zhao, C.F. Caiafa, D.P. Mandic, Z.C. Chao, Y. Nagasaka, N. Fujii, L. Zhang, and A. Cichocki:
"Higher-order partial least squares (HOPLS): A generalized multi-linear regression method"
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 35(7):1660-1673, (2013).
- 9.Q. Zhao, L. Zhang, and A. Cichocki:
"A tensor-variate Gaussian process for classification of multidimensional structured data"
Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI’13), pp. 1041-1047, (2013).
- 10.Q. Zhao, C. F Caiafa, D. P Mandic, L. Zhang, T. Ball, A. Schulze-Bonhage, and A. Cichocki:
"Multilinear subspace regression: An orthogonal tensor decomposition approach"
Advances in Neural Information Processing Systems 24 (NIPS), pp. 1269-1277, (2011).
- Qibin Zhao
- Team Leader
- Chao Li
- Postdoctoral Researcher
- Ming Hou
- Postdoctoral Researcher
Nihonbashi 1-chome Mitsui Building, 15th floor,
Email: qibin.zhao [at] riken.jp