Publications related to Elastic net regularization

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Volkan Cevher, Ya-Ping Hsieh, Marwa El Halabi, Cong Bang Vu, Van Quang Nguyen

In this paper, we consider composite convex minimization problems. We advocate the merit of considering Generalized Proximal gradient Methods (GPM) where the norm employed is not Euclidean. To that end, we show the tractability of the general proximity ope ...

2017

Smoothing technique for nonsmooth composite minimization with linear operator

Volkan Cevher, Van Quang Nguyen

We introduce and analyze an algorithm for the minimization of convex functions that are the sum of differentiable terms and proximable terms composed with linear operators. The method builds upon the recently developed smoothed gap technique. In addition t ...

2017

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

Xin Liu, Hanjie Pan, Feng Xue, Jiaqi Liu

Recently, the type of compound regularizers has become a popular choice for signal reconstruction. The estimation quality is generally sensitive to the values of multiple regularization parameters. In this work, based on BDF algorithm, we develop a data-dr ...

Ieee2017

USSR: An UltraSound Sparse Regularization framework

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Marcel Arditi, Adrien Georges Jean Besson, Florian Martinez

Ultrafast ultrasound (US) imaging uses unfocused waves to insonify the whole medium of interest at once, allowing pulse-echo US imaging to achieve very high frame rates, at the cost of a lower image quality. In this paper, we present USSR, an UltraSound Sp ...

Ieee2017

DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy

Michaël Unser, Daniel Sage, Laurène Donati, Cédric René Jean Vonesch, Ferréol Arnaud Marie Soulez, Denis Fortun, Guillaume Schmit

Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation. Deconvolution is widely used to restore fine details of 3D biologi ...

2017

Comparing molecules and solids across structural and alchemical space

Michele Ceriotti, Sandip De

Evaluating the (dis)similarity of crystalline, disordered and molecular compounds is a critical step in the development of algorithms to navigate automatically the configuration space of complex materials. For instance, a structural similarity metric is cr ...

Royal Society of Chemistry2016

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

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Emrah Bostan

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

EPFL2016

Morphological component analysis for sparse regularization in plane wave imaging

Jean-Philippe Thiran, Yves Wiaux, Dimitris Perdios, Eric Bezzam, Rafael Eduardo Carrillo Rangel, Marcel Arditi, Adrien Georges Jean Besson

Classical ultrasound image reconstruction mainly relies on the well-known delay-and-sum (DAS) beamforming for its simplicity and real-time capability. Sparse regularization methods propose an alternative to DAS which lead to a better inversion of the ill-p ...

Ieee2016

Fast rates by transferring from auxiliary hypotheses

Ilja Kuzborskij

In this work we consider the learning setting where, in addition to the training set, the learner receives a collection of auxiliary hypotheses originating from other tasks. We focus on a broad class of ERM-based linear algorithms that can be instantiated ...

2016

Elastic net regularization

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General Proximal Gradient Method: A Case for Non-Euclidean Norms

Smoothing technique for nonsmooth composite minimization with linear operator

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

USSR: An UltraSound Sparse Regularization framework

DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy

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

Morphological component analysis for sparse regularization in plane wave imaging

Fast rates by transferring from auxiliary hypotheses

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Smoothing technique for nonsmooth composite minimization with linear operator

USSR: An UltraSound Sparse Regularization framework

Optimization Of Compound Regularization Parameters Based On Stein'S Unbiased Risk Estimate

DeconvolutionLab2: An Open-Source Software for Deconvolution Microscopy

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

Morphological component analysis for sparse regularization in plane wave imaging

Fast rates by transferring from auxiliary hypotheses