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For every part, we compute PCA (keeping principal components that explain the 98% of data variance).
This layer consists of a few selected principal components that perform the function of hidden nodes.
Overall accuracy could be increased by optimizing the number of principal components that are used (see Fig. 9).
For every part, we compute PCA (keeping principal components that preserve the 98% of the data variance).
The dimension of the original data can be reduced by retaining only a small number of principal components that describe the predefined amount of variability.
Figure 5 shows a single-band image at 700 nm and the first five principal components that are extracted from the corresponding hyperspectral image.
Principal components that characterize >95% of covariate space variability were then integrated and classified using an ISODATA (Iterative Self-Organizing Data) unsupervised technique.
It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns.
In our climatic PCA analyses, the five climatic variables collapsed into two principal components that together explained 98.0% of the variance.
PCA allows for the reduction of data complexity by discovering a number of principal components that define most of the data variability.
The resulting smallest subset of gene-level principal components that accounted for at least 90% of the SNP variability was included in regression models, and gene-specific associations were evaluated using a multiple degree of freedom likelihood ratio test.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com