Tensor Learning Code

by Tensor Learning Team, RIKEN AIP

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.

For more information, please refer to our homepage [Link].

Open Source Code





  • Tensor Ring Decomposotion Toolbox (Matlab) [Code]
  • Gan PU: Generative Adversarial Positive-Unlabeled Learning (Python) [Code]
  • BCPF: Bayesian CP Factorization for Tensor Completion (Matlab) [Github]
  • BRTF: Bayesian Robust Tensor Factorization (Matlab) [Github]
  • T3C: Tensor-Train Tensor Completion (Matlab) [Github]