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The spectral expansion is mathematically analyzed using the eigenvalue set of the transport operator.
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Using the eigenvalue decomposition of, (36).
Factors were retained using the eigenvalue >1 criterion.
Using the eigenvalue criterion, two factors were extracted.
Finally, the activity profiles of transcription factors are estimated using the eigenvalue decomposition procedure.
The adequate (smallest) number of factors was determined by using the Eigenvalue criteria.
PCA derives orthogonal components from the set of exposure variables by making full use of the variability in the data by using the eigenvalues and eigenvectors of the exposure correlation matrix.
Thirdly, the dynamic behavior of a system can be understood using the eigenvalues and eigenvectors.
All the methods examine the model stability using the eigenvalues of the constructed system matrix.
Principal components analysis, with varimax rotation produced a 7 factor solution using the eigenvalues >1 criteria.
Therefore, instead of considering the eigenvalue set, an eigenvalue distribution description is used as explained in Section 2.1.
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