We propose a novel adaptive algorithm for TT decomposition of streaming tensors whose slices are serially acquired over time. By leveraging the alternating minimization framework, our estimator minimizes an exponentially weighted least-squares cost function in an efficient way.
- Our code requires the Tensor Toolbox which is already attached in this repository.
- MATLAB R2019a
Quick Start: Just run the file DEMO.m
Effect of the noise level and time-varying factors on the performance of our method
Performance of three TT decomposition algorithms in a time-varying scenarioThis code is free and open source for research purposes. If you use this code, please acknowledge the following paper.
[1] L.T. Thanh, K. Abed-Meraim, N.L. Trung, and R Boyer. "Adaptive Algorithms for Tensor Train Decomposition of Streaming Tensors". European Signal Processing Conference (EUSIPCO), 2020.