From Multiset Events to Signal Restoration via Tensor Decomposition Based Separation Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The vision and development of blind source separation (BSS) considering the multi-dimensional components attract growing interests in the research community. The single-set BSS methods cannot address the challenge of aligning the extracted components across different datasets, because they neglect the dependence information across datasets. In this paper, we propose to apply the tensor theory to develop a novel structure for the BSS technique that allows us to discover the multi-set components and meaningful spatio-temporal structures of data. The structure is stacked of covariance matrices with temporal information, in terms of limited storage, using the tensor theory that can efficiently encapsulate and compress large-scale data into a compact format. In addition, we discuss how our results provide unique decomposition under such conditions from the theoretical perspective. The experiments designed on the signal with time variation (multiset), which is not identifiable when each mixture is considered individually. It can be solved to restate as a tensor structure of their spatial and temporal correlation matrices. Compared with the other three classical algorithms, the proposed algorithm consistently shows high accuracy.

Original languageEnglish
Title of host publicationICC 2021 - IEEE International Conference on Communications, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728171227
Publication statusPublished - 2021 Jun
Event2021 IEEE International Conference on Communications, ICC 2021 - Virtual, Online, Canada
Duration: 2021 Jun 142021 Jun 23

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


Conference2021 IEEE International Conference on Communications, ICC 2021
CityVirtual, Online


  • Joint blind source separation
  • multiset data
  • second-order statistic
  • tensor decomposition

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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