Advantages and Limitations of PCA

PCA offers several advantages:

• Dimensionality reduction: PCA reduces the number of variables while preserving the most important information.
• Improved data interpretation: By transforming the data into principal components, the underlying patterns become more evident.
• Noise reduction: PCA helps in removing noise and outliers from the data.

However, PCA also has limitations:

• Linear relationships: PCA assumes linear relationships between variables, which may not be suitable for datasets with complex nonlinear patterns.
• Interpretability: While PCA provides a concise representation of the data, the interpretation of principal components may not always be straightforward.