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Using the sparse representation of u t, i.e., z t, the optimization problem of (14) can be formulated as a sparsity and non-negativity constrained optimization problem.
The type I error rate of the correlation in the control population is used as a sparsity threshold; only those edges with a correlation significance level of P < 0.05 are considered as linked.
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For the former cases, we select the well-known discrete wavelet transform as a sparsifying transform, whereas for the latter, we resort to the graph-based transform, originally established for depth map encoding, as a sparsity-achieving representation within the reconstruction procedure.
In [10], a sparsity as well as a ridge-penalized, non-negativity constrained, ordinary least squares method is used to estimate the spatial rainfall map from linear path-averaged rainfall intensities, albeit for a single snapshot.
We define ρ as a global sparsity parameter for all hidden units, typically a small value close to zero.
We propose a sparsity function as follows (14).
As a consequence, without imposing a sparsity prior on x, the set of equations y = Ax is underdetermined and admits many solutions.
The method we propose is based on l1-regularized linear regression known as lasso [ 21] that yields a sparsity of variable selection.
Specifically, we address representatives selection as a row-sparsity regularized minimization problem which can be effectively solved via convex programming.
The reconstruction of these structured signals is usually performed with an iterative least-squares optimization in which a sparsity-promoting norm serves as an additional constraint, or regularizer.
As a consequence, basis sparsity is a high virtue and the choice of the basis can be critical.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com