268 citations · 1.4k across the 80 of their papers we have counts for
7 papers · 2 filters
Sparse Support Recovery with Non-smooth Loss Functions
Kévin Degraux, Gabriel Peyré, Jalal M. Fadili +1
In this paper, we study the support recovery guarantees of underdetermined sparse regression using the -norm as a regularizer and a non-smooth loss function for data fideli…
Through the Haze: a Non-Convex Approach to Blind Gain Calibration for Linear Random Sensing Models
Valerio Cambareri, Laurent Jacques
Computational sensing strategies often suffer from calibration errors in the physical implementation of their ideal sensing models. Such uncertainties are typically addressed by us…
A Greedy Blind Calibration Method for Compressed Sensing with Unknown Sensor Gains
Valerio Cambareri, Amirafshar Moshtaghpour, Laurent Jacques
The realisation of sensing modalities based on the principles of compressed sensing is often hindered by discrepancies between the mathematical model of its sensing operator, which…
Improving the Correlation Lower Bound for Simultaneous Orthogonal Matching Pursuit
Jean-François Determe, Jérôme Louveaux, Laurent Jacques +1
The simultaneous orthogonal matching pursuit (SOMP) algorithm aims to find the joint support of a set of sparse signals acquired under a multiple measurement vector model. Critical…
Time for dithering: fast and quantized random embeddings via the restricted isometry property
Laurent Jacques, Valerio Cambareri
Recently, many works have focused on the characterization of non-linear dimensionality reduction methods obtained by quantizing linear embeddings, e.g., to reach fast processing ti…
A Non-Convex Blind Calibration Method for Randomised Sensing Strategies
Valerio Cambareri, Laurent Jacques
The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that…
On the Noise Robustness of Simultaneous Orthogonal Matching Pursuit
Jean-François Determe, Jérôme Louveaux, Laurent Jacques +1
In this paper, the joint support recovery of several sparse signals whose supports present similarities is examined. Each sparse signal is acquired using the same noisy linear meas…