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Table 16 SCA steps

From: A unified approach to sparse signal processing

1. Consider the model x=A·s; we need a linear transformation that applies to both sides of the equation to yield a new sparse source vector.

2. Estimate the mixing matrix A. Several approaches are presented for this step, such as natural gradient ICA approaches, and clustering techniques with variants of k-means algorithm [18, 187].

3. Estimate the source representation based on the sparsity assumption. A majority of proposed methods are primarily based on minimizing some norm or pseudo-norm of the source representation vector. The most effective approaches are Matching Pursuit [38, 187], Basis Pursuit, [85, 178, 188, 189], FOCUSS [46], IDE [73] and Smoothed â„“0-norm [47].