Publications related to Regularized least squares

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

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

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Dimitri Nestor Alice Van De Ville, Stephan Morgenthaler, Thomas William Arthur Bolton, Maria Giulia Preti, Djalel Eddine Meskaldji

An important question in neuroscience is to reveal the relationship between individual performance and brain activity. This could be achieved by applying model regression techniques, in which functional connectivity derived from resting-state functional ma ...

Ieee2016

Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider

Mikael Johan Kuusela

This thesis studies statistical inference in the high energy physics unfolding problem, which is an ill-posed inverse problem arising in data analysis at the Large Hadron Collider (LHC) at CERN. Any measurement made at the LHC is smeared by the finite reso ...

EPFL2016

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

What crowding can tell us about object representations

Michael Herzog, Aaron Michael Clarke, Mauro Manassi

In crowding, perception of a target usually deteriorates when flanking elements are presented next to the target. Surprisingly, adding further flankers can lead to a release from crowding. In previous work we showed that, for example, vernier offset discri ...

Association for Research in Vision and Ophthalmology (ARVO)2016

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Michaël Unser

Ill-posed inverse problems are often constrained by imposing a bound on the total variation of the solution. Here, we consider a generalized version of total-variation regularization that is tied to some differential operator L. We then show that the gener ...

OSA2016

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

Ali H. Sayed

In many fields, and especially in the medical and social sciences and in recommender systems, data are gathered through clinical studies or targeted surveys. Participants are generally reluctant to respond to all questions in a survey or they may lack info ...

IEEE2016

Joint absorption and phase retrieval in grating-based x-ray radiography

Michaël Unser, Emrah Bostan, Masih Nilchian, Marco Stampanoni, Mohammad Reza Nilchiyan

Given the raw absorption and differential phase-contrast images obtained from a grating-based x-ray radiography, we formulate the joint denoising of the absorption image and retrieval of the non-differential phase image as a regularized inverse problem. Th ...

Optical Society of America2016

Performance limits of single-agent and multi-agent sub-gradient stochastic learning

Ali H. Sayed, Bicheng Ying

This work examines the performance of stochastic sub-gradient learning strategies, for both cases of stand-alone and networked agents, under weaker conditions than usually considered in the literature. It is shown that these conditions are automatically sa ...

IEEE2016

Total Variation Regularization of Shape Signals

Martin Kurt Storath

This paper introduces the concept of shape signals, i.e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals. The proposed formulation can be seen as the shape-va ...

IEEE2015

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Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider

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

What crowding can tell us about object representations

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

Joint absorption and phase retrieval in grating-based x-ray radiography

Performance limits of single-agent and multi-agent sub-gradient stochastic learning

Total Variation Regularization of Shape Signals

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Characterization of the Solution of Linear Inverse Problems with Generalized TV Regularization

Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Joint absorption and phase retrieval in grating-based x-ray radiography

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

Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider

What crowding can tell us about object representations

Performance limits of single-agent and multi-agent sub-gradient stochastic learning

Total Variation Regularization of Shape Signals