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However, conventional ICA-based approaches cannot be used for underdetermined cases, where the number of sensors is less than the number of sources, and sparseness-based methods are highly dependent on the sparsity of the mixing signals.
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In our experience, this number depends heavily on the sparsity of the influence matrix [ 12].
More importantly, the optimal value λopt depends approximately monotonously on the sparsity of the training set.
The computational complexity of TSA is dependent on n, sparsity k, and a chosen constant l.
Importantly, increasing α also increases the runtime of the algorithm significantly, such that one often needs to compromise on the sparsity of the network to keep computations feasible.
However, for such cases the cross-validation errors are not as good as the one obtained through the learning algorithm on the sparsity parameter.
Specifically, the number of measurements is generally proportional to the signal dimension and is weakly dependent on target density sparsity.
Considering the discrete weak form and its solution (as we show later), this weakly penalized term requires matrix matrix products which are (1) expensive, (2) nonlinearly dependent on the solution and (3) generally of a more dense sparsity than the standard penalty system.
On the other hand, the SFFT-related algorithms demonstrate reliable operation for specific input parameter combinations, i.e., they are dependent on the combination of the dimension N of the input signal x, and the sparsity factor k s.
The reduction is achieved by using random sampling and capitalizing on the sparsity of vibration signals in the frequency domain.
3. Estimate the source representation based on the sparsity assumption.
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