Robust group lasso: model and recoverability
From MaRDI portal
Publication:1790464
DOI10.1016/j.laa.2018.06.034zbMath1402.62110OpenAlexW2887599264WikidataQ129430098 ScholiaQ129430098MaRDI QIDQ1790464
Zhu Han, Xiaohan Wei, Qing Ling
Publication date: 2 October 2018
Published in: Linear Algebra and its Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.laa.2018.06.034
Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Robustness and adaptive procedures (parametric inference) (62F35) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Cites Work
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- User-friendly tail bounds for sums of random matrices
- Random sampling of sparse trigonometric polynomials
- Compressed sensing and matrix completion with constant proportion of corruptions
- RIPless compressed sensing from anisotropic measurements
- Exact matrix completion via convex optimization
- Exact Recoverability From Dense Corrupted Observations via $\ell _{1}$-Minimization
- Robust principal component analysis?
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Block-Sparse Signals: Uncertainty Relations and Efficient Recovery
- Group-Lasso on Splines for Spectrum Cartography
- DOA Estimation Using a Greedy Block Coordinate Descent Algorithm
- Robust Recovery of Signals From a Structured Union of Subspaces
- Rank Awareness in Joint Sparse Recovery
- A Probabilistic and RIPless Theory of Compressed Sensing
- Robust Frame Based X-Ray CT Reconstruction
- Recovering Low-Rank Matrices From Few Coefficients in Any Basis
- Dense Error Correction Via $\ell^1$-Minimization
- Corrupted Sensing: Novel Guarantees for Separating Structured Signals
- A sparse signal reconstruction perspective for source localization with sensor arrays
- Model Selection and Estimation in Regression with Grouped Variables
- Compressed sensing
