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The pcanorm-method with 1000 features is not significantly better than chance level (50%%), since it is highly affected by the curse of dimensionality, due to too many principal components with a variance ≈0.
We therefore use PCA as a measure for the complexity of hand configurations, by measuring how many principal components can explain how much variability in the data.
There are as many principal components as variables under analysis.
Second, there is a question of how many principal components are to be analyzed.
The dimensional structure of the QIDS-SR16 was examined by using a parallel analysis to determine how many principal components should be extracted from the data (PCA) [ 34].
The alpha score was used to choose the number of retained principal components and subsequent discriminant functions in order to avoid over-fitting of the data by retaining to many principal components as suggested by the manual.
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Because the number of input variables for GMDH is inadvisable to be too many, principal component analysis (PCA) is used to reduce the dimensions of 12 critical electrical test parameters to a manageable few without much loss of information.
As many principal component loadings would have been needed to account for the variation within this collection, we decided to include STRUCTURE output into the association model to correct for population structure.
Many principal component analyses conducted in the healthy subjects support the five basic traits.
After selecting SNPs with nearly complete genotypic data that are also in high LD with many other SNPs, we performed principal components analyses on those SNPs located within the breakpoints of each of the three common inversions.
Many authors use principal component analysis (PCA) [65] for reducing the dimensionality of the feature space [152].
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