Highest values for each PC factor are shown in bold, and the percentage of variation in the response variables explained by each PC factor is shown in parentheses.
Principal component analysis is a versatile tool to reduce dimensionality which has wide applications in statistics and machine learning. It is particularly useful for modeling data in high-dimensiona
The table shows factor loadings of the acoustic parameters on the principal components showing eigenvalues >1 (PC1–PC4) extracted from the PCA. Note: *heaviest factor loadings (r>0.70).
