• AVITECH Research Group

  • Research Projects

    Tensor Tracking

    Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however non-trivial due to several inherent problems of real-time stream processing.

    In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analytics perspective.

     

    Selected Publications

    L.T. Thanh, K. Abed-Meraim, N. L. Trung, & A. Hafian.  A Novel Recursive Least-Squares Adaptive Method For Streaming Tensor-Train Decomposition With Incomplete Observations. Signal Processing (SP), 2024.

    L.T. Thanh, K. Abed-Meraim, N. L. Trung, & A. Hafiane. A Contemporary and Comprehensive Survey on Streaming Tensor Decomposition. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023.

    L.T. Thanh, K. Abed-Meraim, N. L. Trung, & A. Hafiane. Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors. Cell Patterns, 2023.

    L.T. Thanh, K. Abed-Meraim, N. L. Trung, & A. Hafiane. Robust Tensor Tracking with Missing Data and Outliers: Novel Adaptive CP Decomposition and Convergence Analysis. IEEE Transactions on Signal Processing (TSP), 2022.

    L.T. Thanh, K. Abed-Meraim, P. Ravier, & O. Buttelli.  A Novel Tensor Tracking Algorithm For Block-Term Decomposition of Streaming Tensors. IEEE Statistical Signal Processing Workshop (SSP), 2023.

    L.T. Thanh, K. Abed-Meraim, N. L. Trung & A. Hafiane.  Robust Tensor Tracking With Missing Data Under Tensor-Train Format. European Signal Processing Conference (EUSIPCO), 2022.

    L.T. Thanh, T. T. Duy, K. Abed-Meraim, N. L. Trung, & A. Hafiane. Robust Online Tucker Dictionary Learning from Multidimensional Data Streams. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA-ASC), 2022.

    L.T. Thanh, K. Abed-Meraim, N. L. Trung, & A. Hafiane. A Fast Randomized Adaptive CP Decomposition for Streaming Tensors. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.

    Viet-Dung Nguyen, Karim Abed-Meraim, and Nguyen Linh-Trung. Second-order optimization based adaptive PARAFAC decomposition of three-way tensors. Digital Signal Processing, 63:100–111, April 2017.

    SAME CATEGORY

    Signal Processing for Machine Learning

    Machine learning for signal processing

    Tensor Decomposition for Medicine

    Tensor decomposition (TD) is a powerful mathematical framework for analyzing high-dimensional and multi-way data, such as medical images, biomedical signals, and clinical measurements. Unlike conventional matrix-based methods, TD represents a tensor as a combination of lower-dimensional factor matrices or core tensors, enabling the extraction of latent structures while preserving the intrinsic multi-dimensional relationships within the […]