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Sparse solutions of underdetermined linear systems of equations are widely used in different fields of signal processing. This problem can also be seen as a sparse decomposition problem. Traditional sparse decomposition gives the same priority to all atoms for being included in the decomposition or not. However, in some applications, one may want to assign different priorities to different atoms for...
Score Function Difference (SFD) is a recently proposed “gradient” for mutual information which can be used in Blind Source Separation algorithms based on minimization of mutual information. To be applied to practical problems, SFD must be estimated from the data samples. In this paper, a new method for estimating SFD is proposed. To compare the performance of this new estimator with other proposed...
In this paper, the performance of the gradient method based on Score Function Difference (SFD) in the separation of i.i.d. and periodic signals will be investigated. We will see that this algorithm will separate periodic signals better than i.i.d. ones. By using this experimental result and the fact that voiced frames of speech signals are approximately periodic, a modified algorithm named VDGaradient...
In this paper we propose a fast and efficient algorithm for learning overcomplete dictionaries. The proposed algorithm is indeed an alternative to the well-known K-Singular Value Decomposition (K-SVD) algorithm. The main drawback of K-SVD is its high computational load especially in high-dimensional problems. This is due to the fact that in the dictionary update stage of this algorithm an SVD is performed...
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