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To overcome this problem, dimension reduction methods are applied to reduce the dimensionality from p to q with q ≪ p. Dimension reduction usually consists of two types of methods, feature selection and feature extraction [ 4].
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This will lead to optimal solutions, if the problem dimension is small.
This drawback is compounded for importance sampling methods, since the number of required samples increases exponentially with the problem dimension.
The proposed method provides fast local convergence and scales well with respect to the problem dimension.
We develop methods to significantly reduce the problem dimension by exploiting the problem characteristics and network structure.
This last equation is chosen arbitrary and implies a scale relationship between the θk that reduce by four the problem dimension.
The number of iterations and vertices vary with the problem dimension and the shape of the feasible region.
If several cores are used, and the problem dimension is scaled proportionally, the routine still achieves a similar performance rate.
The wavelet fraction of 0.60 indicates that the problem dimension is reduced to 60%% of its original size.
Through the application of this filter pipeline the preselection of epitopes is adjusted to the experimental setup and the problem dimension is significantly reduced.
The inclusion of epitopes of length five increased the problem dimension considerably, because of the much larger number of potential epitope sequences.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com