141 citations · 1.7k across the 117 of their papers we have counts for
4 papers · 2 filters
Typical -recovery limit of sparse vectors represented by concatenations of random orthogonal matrices
Yoshiyuki Kabashima, Mikko Vehkapera, Saikat Chatterjee
We consider the problem of recovering an -dimensional sparse vector $\vm{x}$ from its linear transformation $\vm{y}=\vm{D} \vm{x}$ of dimension. Minimizing the -…
Compressed sensing with sparse, structured matrices
Maria Chiara Angelini, Federico Ricci-Tersenghi, Yoshiyuki Kabashima
In the context of the compressed sensing problem, we propose a new ensemble of sparse random matrices which allow one (i) to acquire and compress a ρ0-sparse signal of length N in…
Analysis of Sparse Representations Using Bi-Orthogonal Dictionaries
Mikko Vehkaperä, Yoshiyuki Kabashima, Saikat Chatterjee +3
The sparse representation problem of recovering an N dimensional sparse vector x from M < N linear observations y = Dx given dictionary D is considered. The standard approach is to…
A study of the universal threshold in the L1 recovery by statistical mechanics
Koujin Takeda, Yoshiyuki Kabashima
We discuss the universality of the L1 recovery threshold in compressed sensing. Previous studies in the fields of statistical mechanics and random matrix integration have shown tha…