SBTD: Block term decomposition of streaming tensors

We propose a novel tensor tracking method called SBTD for factorizing tensors derived from multidimensional data streams under the BTD format. Thanks to the alternating optimization framework, SBTD first applies a regularized least-squares solver to estimate the temporal factor of the underlying streaming tensor. Then, SBTD adopts an adaptive filter to track the nontemporal tensor factors over time by minimizing a weighted least-squares cost function.

Demo

Please run

• demo_comparison.m: To illustrate the performance of SBTD in comparsion with BTD-ALS and onlineBTD.
• demo_noise.m: To illustrate the effect of Gaussian noise on the performance of SBTD
• demo_time_varying.m: To illustrate the ability of SBTD in non-stationary environments

Reference

This 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, P. Ravier, O. Buttelli . "A Novel Tensor Tracking Algorithm For Block-Term Decomposition of Streaming Tensors". Proc. IEEE SSP, 2023.