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Sparsity parameters are selected using the permutation approach presented in [18] of which the code is provided in PMA package.
The sparsity parameters of the SI-LMS/RLS algorithms are set to ρ=0.07 and ε=10.
In the time-varying scenario, the sparsity parameters of the SI-LMS and SI-RLS algorithms are set to ρ=6×10−3 and ε=10.
In the static scenario, the sparsity parameters of the SI-LMS/SI-RLS algorithms are set to ρ=4×10−3 and ε=10.
To assess the appropriate sparsity parameters, two different λ sequences were tested on the simulated rs-fMRI datasets.
The parameters in each method (e.g. regularization parameters, sparsity parameters and number of components) were optimized by performing cross-validation.
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Sparsity parameter (rho = 0.25) 4.
(6) This experiment tests the influence of the sparsity parameter ι.
All the algorithms are not very sensitive to the sparsity parameter.
We define ρ as a global sparsity parameter for all hidden units, typically a small value close to zero.
We would like to enforce the average activation equal to a sparsity parameter ρ, which is close to zero.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com