A Fixed Point Iterative Method for Low $n$-Rank Tensor Pursuit

DOI10.1109/TSP.2013.2254477zbMath1393.90090OpenAlexW2064683861MaRDI QIDQ4578584

Zheng-Hai Huang, Lei Yang, Xian-Jun Shi

Publication date: 22 August 2018

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tsp.2013.2254477

Mathematics Subject Classification ID

Convex programming (90C25) Derivative-free methods and methods using generalized derivatives (90C56) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Multilinear algebra, tensor calculus (15A69)

Related Items (10)

Minimum \( n\)-rank approximation via iterative hard thresholding ⋮ Continuity, differentiability and semismoothness of generalized tensor functions ⋮ Parallel active subspace decomposition for tensor robust principal component analysis ⋮ Low Tucker rank tensor recovery via ADMM based on exact and inexact iteratively reweighted algorithms ⋮ Low-rank matrix recovery problem minimizing a new ratio of two norms approximating the rank function then using an ADMM-type solver with applications ⋮ $N$-Dimensional Tensor Completion for Nuclear Magnetic Resonance Relaxometry ⋮ Iterative \(p\)-shrinkage thresholding algorithm for low Tucker rank tensor recovery ⋮ Tensor completion using total variation and low-rank matrix factorization ⋮ Tensor complementarity problems. I: Basic theory ⋮ A Splitting Augmented Lagrangian Method for Low Multilinear-Rank Tensor Recovery

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