Tensor completion via functional smooth component deflation

Tatsuya Yokota, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

For the matrix/tensor completion problem with very high missing ratio, the standard local (e.g., patch, probabilistic, and smoothness) and global (e.g., low-rank) structure-based methods do not work well. To address this issue, we proposed to use local and global data structures at the same time by applying a novel functional smooth PARAFAC decomposition model for the tensor completion. This decomposition model is constructed as a sum of the outer product of functional smooth component vectors, which are represented by linear combinations of smooth basis functions. A new algorithm was developed by applying greedy deflation and smooth rank-one tensor decomposition. Our extensive experiments demonstrated the high performance and advantages of our algorithm in comparison to existing state-of-the-art methods.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2514-2518
Number of pages5
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 18 May 2016
Externally publishedYes
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • B-spline basis
  • cosine basis
  • greedy deflation
  • smooth component analysis
  • Tensor completion

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