61 citations · 419 across the 59 of their papers we have counts for
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The Linear Model under Mixed Gaussian Inputs: Designing the Transfer Matrix
John T. Flåm, Dave Zachariah, Mikko Vehkaperä +1
Suppose a linear model y = Hx + n, where inputs x, n are independent Gaussian mixtures. The problem is to design the transfer matrix H so as to minimize the mean square error (MSE)…
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 -…
Dynamic Iterative Pursuit
Dave Zachariah, Saikat Chatterjee, Magnus Jansson
For compressive sensing of dynamic sparse signals, we develop an iterative pursuit algorithm. A dynamic sparse signal process is characterized by varying sparsity patterns over tim…
Alternating Least-Squares for Low-Rank Matrix Reconstruction
Dave Zachariah, Martin Sundin, Magnus Jansson +1
For reconstruction of low-rank matrices from undersampled measurements, we develop an iterative algorithm based on least-squares estimation. While the algorithm can be used for any…
Analysis of MMSE Estimation for Compressive Sensing of Block Sparse Signals
Mikko Vehkaperä, Saikat Chatterjee, Mikael Skoglund
Minimum mean square error (MMSE) estimation of block sparse signals from noisy linear measurements is considered. Unlike in the standard compressive sensing setup where the non-zer…
Fusion of Greedy Pursuits for Compressed Sensing Signal Reconstruction
Sooraj K. Ambat, Saikat Chatterjee, K. V. S. Hari
Greedy Pursuits are very popular in Compressed Sensing for sparse signal recovery. Though many of the Greedy Pursuits possess elegant theoretical guarantees for performance, it is…