The nonconvex tensor robust principal component analysis approximation model via the weighted \(\ell_p\)-norm regularization

DOI10.1007/s10915-021-01679-6zbMath1484.62074OpenAlexW3213372190MaRDI QIDQ2053328

Mingqing Xiao, Wen Li, Yannan Chen, Ming-Hui Li

Publication date: 29 November 2021

Published in: Journal of Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10915-021-01679-6

zbMATH Keywords

non-convex optimizationalternating direction method of multiplierslow rank approximation\(\ell_p\)-normtensor singular value decompositiontensor robust principal component analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Numerical mathematical programming methods (65K05) Multilinear algebra, tensor calculus (15A69) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items (4)

A generalized non-convex method for robust tensor completion ⋮ Provable stochastic algorithm for large-scale fully-connected tensor network decomposition ⋮ Multi-dimensional image recovery via fully-connected tensor network decomposition under the learnable transforms ⋮ Nonconvex multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix*

Uses Software

• BSDS
• tproduct

Cites Work

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