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Successive Concave Sparsity Approximation for Compressed Sensing
Mohammadreza Malek-Mohammadi, Ali Koochakzadeh, Massoud Babaie-Zadeh +2
In this paper, based on a successively accuracy-increasing approximation of the norm, we propose a new algorithm for recovery of sparse vectors from underdetermined measur…
Iterative Concave Rank Approximation for Recovering Low-Rank Matrices
Mohammadreza Malek-Mohammadi, Massoud Babaie-Zadeh, Mikael Skoglund
In this paper, we propose a new algorithm for recovery of low-rank matrices from compressed linear measurements. The underlying idea of this algorithm is to closely approximate the…
Upper Bounds on the Error of Sparse Vector and Low-Rank Matrix Recovery
Mohammadreza Malek-Mohammadi, Cristian R. Rojas, Magnus Jansson +1
Suppose that a solution to an underdetermined linear system is given. is approximately sparse…
Performance Guarantees for Schatten- Quasi-Norm Minimization in Recovery of Low-Rank Matrices
Mohammadreza Malek-Mohammadi, Massoud Babaie-Zadeh, Mikael Skoglund
We address some theoretical guarantees for Schatten- quasi-norm minimization () in recovering low-rank matrices from compressed linear measurements. Firstly, using…
DOA Estimation in Partially Correlated Noise Using Low-Rank/Sparse Matrix Decomposition
Mohammadreza Malek-Mohammadi, Magnus Jansson, Arash Owrang +2
We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise cova…
Sparse Channel Estimation by Factor Graphs
Rad Niazadeh, Masoud Babaie-Zadeh, Christian Jutten
The problem of estimating a sparse channel, i.e. a channel with a few non-zero taps, appears in various areas of communications. Recently, we have developed an algorithm based on i…
Recovery of Low-Rank Matrices under Affine Constraints via a Smoothed Rank Function
Mohammadreza Malek-Mohammadi, Massoud Babaie-Zadeh, Arash Amini +1
In this paper, the problem of matrix rank minimization under affine constraints is addressed. The state-of-the-art algorithms can recover matrices with a rank much less than what i…
On the error of estimating the sparsest solution of underdetermined linear systems
Massoud Babaie-Zadeh, Christian Jutten, Hosein Mohimani
Let A be an n by m matrix with m>n, and suppose that the underdetermined linear system As=x admits a sparse solution s0 for which ||s0||_0 < 1/2 spark(A). Such a sparse solution is…
Bayesian Hypothesis Testing for Sparse Representation
Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten
In this paper, we propose a Bayesian Hypothesis Testing Algorithm (BHTA) for sparse representation. It uses the Bayesian framework to determine active atoms in sparse representatio…
On the stable recovery of the sparsest overcomplete representations in presence of noise
Massoud Babaie-Zadeh, Christian Jutten
Let x be a signal to be sparsely decomposed over a redundant dictionary A, i.e., a sparse coefficient vector s has to be found such that x=As. It is known that this problem is inhe…
On the Achievability of Cramér-Rao Bound In Noisy Compressed Sensing
Rad Niazadeh, Masoud Babaie-Zadeh, Christian Jutten
Recently, it has been proved in Babadi et al. that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramer-Rao lower bound of the problem.To pr…
Bayesian Cramér-Rao Bound for Noisy Non-Blind and Blind Compressed Sensing
Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten
In this paper, we address the theoretical limitations in reconstructing sparse signals (in a known complete basis) using compressed sensing framework. We also divide the CS to non-…
Sparse Recovery using Smoothed (SL0): Convergence Analysis
Hosein Mohimani, Massoud Babaie-Zadeh, Irina Gorodnitsky +1
Finding the sparse solution of an underdetermined system of linear equations has many applications, especially, it is used in Compressed Sensing (CS), Sparse Component Analysis (SC…
A fast approach for overcomplete sparse decomposition based on smoothed L0 norm
Hossein Mohimani, Massoud Babaie-Zadeh, Christian Jutten
In this paper, a fast algorithm for overcomplete sparse decomposition, called SL0, is proposed. The algorithm is essentially a method for obtaining sparse solutions of underdetermi…