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The graph is further reduced by merging similar datasets to reduce the number of nodes.
This method uses different learning modules in different datasets to reduce the variance.
We applied principal component analyses to the datasets to reduce dimensionality while capturing at least 90% of the total variance/covariance.
A taxonomic flittering procedure was applied on both datasets to reduce oversampling.
Although the preliminary result is encouraging, further studies are needed for evaluating the use of co-registering the supine and prone datasets to reduce false-positive rates in CAD.
For the interactions in DroID, we used a variation of this scoring system in which we combine multiple training datasets to reduce the potential bias of any single training set (Yu, submitted).
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Those variance components may be re-estimated periodically using a reduced dataset to reduce computational burden.
Then, the principal component analysis was performed on the normalized EEG/EMG combined power dataset to reduce the dimension.
Y-STR markers were given a weight calculated as the inverse of the mutation rate in the dataset to reduce network reticulations.
Sequences with >85% amino acid sequence identity within the unambiguously aligned regions were excluded from the dataset to reduce the computational time.
As the relationships among many taxa remain unresolved, 17 species were removed from the dataset to reduce the uncertainty in the topology.
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