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]. |