thank you for your comment, The use of the covariance matrix in forward PCA helps to capture and maximize the variance structure of the original data, which is crucial for identifying the principal components. On the other hand, the correlation matrix is used in inverse PCA to standardize the data, ensuring equal weighting and comparability across different scales. This distinction is particularly important in environmental data analysis, where variables often have different units and scales.
I am curious about why used covariance matrix when you forward PCA but used correlation matrix when inverse it.
thank you for your comment,
The use of the covariance matrix in forward PCA helps to capture and maximize the variance structure of the original data, which is crucial for identifying the principal components. On the other hand, the correlation matrix is used in inverse PCA to standardize the data, ensuring equal weighting and comparability across different scales. This distinction is particularly important in environmental data analysis, where variables often have different units and scales.
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