Publications associées à Lasso (statistiques)

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Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Michaël Unser, Julien René Pierre Fageot, Harshit Gupta

We present a theoretical analysis and comparison of the effect of

ℓ _{ 1 }

versus

ℓ _{ 2 }

regularization for the resolution of ill-posed linear inverse and/or compressed sensing problems. Our formulation covers the most general setting where the s ...

IEEE2016

Lasso (statistiques)

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Acoustic DoA Estimation by One Unsophisticated Sensor

Sparse Gradient Optimization and its Applications in Image Processing

NAND-NOR: A Compact, Fast, and Delay Balanced FPGA Logic Element

Comparing molecules and solids across structural and alchemical space

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Fast rates by transferring from auxiliary hypotheses

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

VITRAIL : Acquisition, Modelling and Rendering of Stained Glass

Sparse Gradient Optimization and its Applications in Image Processing

Representer Theorems for Sparsity-Promoting $ ℓ _{ 1 } $ Regularization

Proximal Multitask Learning Over Networks With Sparsity-Inducing Coregularization

Comparing molecules and solids across structural and alchemical space

Acoustic DoA Estimation by One Unsophisticated Sensor

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Fast rates by transferring from auxiliary hypotheses

NAND-NOR: A Compact, Fast, and Delay Balanced FPGA Logic Element

VITRAIL : Acquisition, Modelling and Rendering of Stained Glass