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Now, let us define three matrices in order to make our following analysis more succinct.
Orthonormality constraints are adopted on the rotation matrices in order to recover structure and motion in a single step.
After calculating the smoothed matrices and for each tile, we collect all the smoothed matrices in order to perform MDF algorithm [19]: (18) (19) (19).
The matrix is decomposed into and matrices in order to minimize the cross entropy (KL divergence) between the matrix and empirical distribution.
Data were collected in the form of dissimilarity matrices in order to easily integrate information over the three datasets (gene expression, chemical structure and molecular targets).
The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus fuzzy partition.
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The algorithm applies neural-networks and position-specific score-matrices in order to assign proper phosphosites to kinases and related families.
Based on kernel function, we propose DCGASC (Discounted Cumulative Gain Anchor Point Set Cover) to select the sub-matrices in order to approximate the original matrix better.
Example of how to modify an H matrix in order to achieve a best recovery capability.
This unfolding transforms a tensor into a matrix in order to highlight one dimension.
Gold nanoparticles (GNPs) were evenly dispersed throughout the CS matrix in order to provide electrical cues.
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