NDLI: Canonical polyadic tensor decomposition in the presence of non Gaussian noise

Content Provider	IEEE Xplore Digital Library
Author	Farias, R.C. Comon, P.
Copyright Year	2015
Description	Author affiliation: GIPSA-Lab., St. Martin d'Hères, France (Farias, R.C.; Comon, P.)
Abstract	In this paper we describe an estimator for the canonical polyadic (CP) tensor model using order statistics of the residuals. The estimator minimizes in an iterative and alternating fashion a dispersion function given by the weighted ranked absolute residuals. Specific choices of the weights lead to either equivalent or approximate versions of the least squares estimator, least absolute deviation estimator or least trimmed squares estimators. For different noise distributions, we present simulations comparing the performance of the pro posed algorithm with the standard least squares estimator. The simulated performance is equivalent in the Gaussian noise case and superior when the noise is distributed according to the Laplacian or Cauchy distributions.
Starting Page	1321
Ending Page	1325
File Size	168436
Page Count	5
File Format	PDF
ISBN	9780992862633
ISSN	20761465
DOI	10.1109/EUSIPCO.2015.7362598
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-31
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	EURASIP
Subject Keyword	Tensor decomposition Tensile stress Least squares approximations Estimation Robustness Data models Robust estimation Yttrium Arrays Order statistics Non Gaussian noise
Content Type	Text
Resource Type	Article

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Canonical polyadic tensor decomposition in the presence of non Gaussian noise