Low-rank tensor completion with fractal-inspired multi-scale energy regularization

Low-rank tensor completion has become a fundamental tool for recovering high-dimensional data from incomplete observations. However, conventional methods rely primarily on algebraic low-rank priors and do not explicitly regulate how signal energy is distributed across scales. This study introduces a...

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Autori principali:	Chuling Wen, Weijie Liang, Chen Xu, Yuru Zou
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2026-03-01
Serie:	Frontiers in Applied Mathematics and Statistics
Soggetti:	ADMM fractal regularization inverse problems low-rank models multi-scale structure tensor completion
Accesso online:	https://www.frontiersin.org/articles/10.3389/fams.2026.1809903/full
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