Sparse tensor recovery Via N-mode fista with support augmentation

Ashley Prater-Bennette, Lixin Shen

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

1 Citation (Scopus)

Abstract

A common approach for performing sparse tensor recovery is to use an N-mode FISTA method. However, this approach may fail in some cases by missing some values in the true support of the tensor and compensating by erroneously assigning nearby values to the support. This work proposes a four-stage method for performing sparse tensor reconstruction that addresses a case where N-mode FISTA may fail by augmenting the support set. Moreover, the proposed method preserves a Tucker-like structure throughout computations for computational efficiency. Numerical results on synthetic data demonstrate that the proposed method produces results with similar or higher accuracy than N-mode FISTA, and is often faster.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1363-1367
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

Fingerprint

Tensors
Recovery
Computational efficiency

Keywords

  • FISTA
  • Iterative soft thresholding
  • Multidimensional compressed sensing
  • Sparse tensors
  • Tucker decomposition

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Cite this

Prater-Bennette, A., & Shen, L. (2019). Sparse tensor recovery Via N-mode fista with support augmentation. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings (pp. 1363-1367). [8646430] (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2018.8646430

Sparse tensor recovery Via N-mode fista with support augmentation. / Prater-Bennette, Ashley; Shen, Lixin.

2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1363-1367 8646430 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).

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

Prater-Bennette, A & Shen, L 2019, Sparse tensor recovery Via N-mode fista with support augmentation. in 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings., 8646430, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1363-1367, 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, Anaheim, United States, 11/26/18. https://doi.org/10.1109/GlobalSIP.2018.8646430
Prater-Bennette A, Shen L. Sparse tensor recovery Via N-mode fista with support augmentation. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1363-1367. 8646430. (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). https://doi.org/10.1109/GlobalSIP.2018.8646430
Prater-Bennette, Ashley ; Shen, Lixin. / Sparse tensor recovery Via N-mode fista with support augmentation. 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1363-1367 (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings).
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