What are the limitations of PCA?
Principal Component Analysis (PCA) might not capture complex data patterns. The resulting principal components can be difficult to interpret and are sensitive to variable scaling and outliers. Additionally, PCA can lead to data loss as it reduces dimensionality, potentially discarding valuable information. Interpretation: PCA is often used for exploratory data analysis, as the principal components can be used to visualize the data and identify patterns. LDA is often used for classification tasks, as the discriminant functions can be used to separate the classes.