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 …

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 …

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 …

A series of recent results shows that if a signal admits a sufficiently sparse representation (in
terms of the number of nonzero coefficients) in an “incoherent” dictionary, this solution is …

In this paper, we consider sparse decomposition (SD) of two-dimensional (2D) signals on
overcomplete dictionaries with separable atoms. Although, this problem can be solved by …

We formulate conditions (k-SCA-conditions) under which we can represent a given (m/spl
times/N)-matrix, X,(data set) uniquely (up to scaling and permutation) as a multiplication of …

We present a Bayesian approach for sparse component analysis (SCA) in the noisy case.
The algorithm is essentially a method for obtaining sufficiently sparse solutions of …

Sparse representations has become an important topic in years. It consists in representing,
say, a signal (vector) as a linear combination of as few as possible components (vectors) …

An overview is given of the role of the sparseness constraint in signal processing problems.
It is shown that this is a fundamental problem deserving of attention. This is illustrated by …

In the last few years, we have witnessed an explosion in applications of sparse
representation, the majority of which share the need for finding sparse solutions of …