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    ATT: Adaptive Algorithms for Tensor Train Decomposition of Streaming Tensors

    Tensor-train (TT) decomposition has been an efficient tool to find low order approximation of large-scale, high-order tensors. Existing TT decomposition algorithms are either of high computational complexity or operating in batch-mode, hence quite inefficient for (near) real-time processing. In this work, 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.

     

    Fig: Effect of the noise level on the performance of TT-FOA.  Fig: Effect of the time-varying factor on the performance of TT-FOA. Fig: Time-varying scenario. Performance of TT algorithms.

    Download

    Our MATLAB code can be downloaded here.

    Requirement

    Our code requires the Tensor Toolbox http://www.tensortoolbox.org/

    Demo

    Quick Start: Run the file DEMO.m

    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, N.L. Trung, R Boyer. Adaptive Algorithms for Tensor Train Decomposition of Streaming Tensors. European Signal Processing Conference (EUSIPCO), 2020. [PDF]. 

    Contact

    LE Trung Thanh

    AVITECH Institute
    VNU University of Engineering and Technology, Vietnam

    Email: thanhletrung@vnu.edu.vn // letrungthanhtbt@gmail.com

     

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