基于高斯积Gamma先验的概率张量序列完备化及自动秩估计matlab代码.zip

# Probabilistic Tensor Train Completion based on Gaussian-product-Gamma Prior, with Automatic Rank Estimation ## Functions - demo_rank_accuracy_test.m Run this demo to test the accuracy of the rank estimation. It randomly generate noise 10 times and tests VITTC for different settings. - demo_image_completion.m Run this demo to test VITTC on image completion. The provided image in "TestImages/missing_rate80_1.mat" is the 'jellybeans' image. - f_tensorfolding/ Includes functions for ket-folding and the improved ket-folding in the reference. - f_vittc/ Includes functions realizing the variational inference algorithms proposed for the probabilistic tensor train model. - f_datageneration/ Includes functions that generate synthetic data. - f_perfevaluate/ Includes functions that evaluate the performance of the recovered tensor. - TestImages/ A 'jellybeans' image, and a mask with 80% entries missing - experiment results/ A folder used for storing results. ### Reference [1] Xu, L., Cheng, L., Wong, N., & Wu, Y. C. (2020). Learning tensor train representation with automatic rank determination from incomplete noisy data. arXiv preprint arXiv:2010.06564. [2] Xu, L., Cheng, L., Wong, N., & Wu, Y. C. (2021, December). Overfitting Avoidance in Tensor Train Factorization and Completion: Prior Analysis and Inference. In 2021 IEEE International Conference on Data Mining (ICDM) (pp. 1439-1444). IEEE. [3] Xu, L., Cheng, L., Wong, N., & Wu, Y. C. (2023). Tensor train factorization under noisy and incomplete data with automatic rank estimation. Pattern Recognition, 141, 109650.