From: A novel fixed-point algorithm for constrained independent component analysis
Step 1. Whiten the observed data to make it uncorrelated and unit variance; | |
Step 2. Initialize the demixing matrix as an identity matrix, μn = 0, γn to be a small positive valued number, set ρn and G; | |
Step 3. If the reference is available, update w based on (7); otherwise, update w based on the fixed-point iteration of original ncFastICA; | |
Step 4. Orthogonalize the demixing matrix W; | |
Step 5. Repeat steps 3 and 4 until convergence; | |
Step 6. Compute the demixing matrix \( \overline{\mathbf{W}}={\left({\mathbf{W}}^H\mathbf{V}\right)}^H \) and obtain the recovered signal \( \widehat{\mathbf{s}}={\overline{\mathbf{W}}}^H\mathbf{z} \). |