Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains

Authors

Clémence Prévost (ORCID: https://orcid.org/0000-0001-9359-4626)
Pierre Chainais (ORCID: https://orcid.org/0000-0003-4377-7584)

Publication Details

Journal: Signal Processing
Published: 2025-03-30
DOI: https://doi.org/10.1016/j.sigpro.2025.110001
Citations: 2
Primary Topic: Tensor decomposition and applications
Type: article
Field-Weighted Citation Impact: 3.07

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article

Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains

Clémence Prévost, Pierre Chainais

Mar 30, 2025

2 citations

Signal Processing

Tensor decomposition and applications

3.07

article

Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains

Clémence Prévost, Pierre Chainais

March 30, 2025article en

2 citations

Abstract

No abstract available for this paper.

Signal ProcessingVol. 234

Openalex Percentile: Top 10%

Tensor decomposition and applications

3.07

DOI Open Access PDF OpenAlex

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Optimal estimation of the canonical polyadic decomposition from low-rank tensor trains — Clémence Prévost, Pierre Chainais · Signal Processing (2025) | TGRS Research Map | TGRS