Fast Sparse Representation Based on Smoothed l0 Norm
نویسندگان
چکیده
In this paper, a new algorithm for Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations. The solution obtained by the proposed algorithm is compared with the minimum `-norm solution achieved by Linear Programming (LP). It is experimentally shown that the proposed algorithm is about two orders of magnitude faster than the state-of-the-art `-magic, while providing the same (or better) accuracy.
منابع مشابه
Complex-valued sparse representation based on smoothed l0 norm
In this paper we present an algorithm for complex-valued sparse representation. In our previous work we presented an algorithm for Sparse representation based on smoothed norm. Here we extend that algorithm to complex-valued signals. The proposed algorithm is compared to FOCUSS algorithm and it is experimentally shown that the proposed algorithm is about two or three orders of magnitude faster ...
متن کاملJoint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar
Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-comp...
متن کاملImage Super-Resolution Based on Sparsity Prior via Smoothed l0 Norm
In this paper we aim to tackle the problem of reconstructing a high-resolution image from a single low-resolution input image, known as single image super-resolution. In the literature, sparse representation has been used to address this problem, where it is assumed that both low-resolution and high-resolution images share the same sparse representation over a pair of coupled jointly trained di...
متن کاملNonnegative Matrix Factorization on Orthogonal Subspace with Smoothed L0 Norm Constrained
It is known that the sparseness of the factor matrices by Nonnegative Matrix Factorization can influence the clustering performance. In order to improve the ability of the sparse representations of the NMF, we proposed the new algorithm for Nonnegatie Matrix Factorization, coined nonnegative matrix factorization on orthogonal subspace with smoothed L0 norm constrained, in which the generation o...
متن کاملApproximate Sparse Decomposition Based on Smoothed L0-Norm
In this paper, we propose a method to address the problem of source estimation for Sparse Component Analysis (SCA) in the presence of additive noise. Our method is a generalization of a recently proposed method (SL0), which has the advantage of directly minimizing the ℓ 0-norm instead of ℓ 1-norm, while being very fast. SL0 is based on minimization of the smoothed ℓ 0-norm subject to As = x. In...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2007