Matrix and Tensor Completion in Multiway Delay Embedded Space Using Tensor Train, with Application to Signal Reconstruction

Farnaz Sedighin, Andrzej Cichocki, Tatsuya Yokota, Qiquan Shi

    Результат исследований: Вклад в журналСтатьярецензирование

    11 Цитирования (Scopus)

    Аннотация

    In this paper, the problem of time series reconstruction in a multiway delay embedded space using Tensor Train decomposition is addressed. A new algorithm has been developed in which an incomplete signal is first transformed to a Hankel matrix and in the next step to a higher order tensor using extended Multiway Delay embedded Transform. Then, the resulting higher order tensor is completed using low rank Tensor Train decomposition. Comparing to previous Hankelization approaches, in the proposed approach, blocks of elements are used for Hankelization instead of individual elements, which results in producing a higher order tensor. Simulation results confirm the effectiveness and high performance of the proposed completion approach. Although in this paper we focus on single time series, our method can be straightforwardly extended to reconstruction of multivariate time series, color images and videos.

    Язык оригиналаАнглийский
    Номер статьи9078764
    Страницы (с-по)810-814
    Число страниц5
    ЖурналIEEE Signal Processing Letters
    Том27
    DOI
    СостояниеОпубликовано - 2020

    Fingerprint

    Подробные сведения о темах исследования «Matrix and Tensor Completion in Multiway Delay Embedded Space Using Tensor Train, with Application to Signal Reconstruction». Вместе они формируют уникальный семантический отпечаток (fingerprint).

    Цитировать