What are the limitations of PCA in machine learning?

What are the limitations of PCA in machine learning?

PCA, or Principal Component Analysis, has several limitations, including sensitivity to outliers, loss of interpretability, and the assumption of linearity.

Can you explain how sensitivity to outliers affects PCA?

Certainly! PCA is sensitive to outliers because it tries to maximize variance. Outliers can disproportionately influence the direction of the principal components, leading to misleading results.

What does loss of interpretability mean in the context of PCA?

Loss of interpretability means that once data is transformed into principal components, it can be challenging to understand what these components represent in the original feature space.

Are there any alternatives to PCA that address these limitations?

Yes, alternatives like t-SNE and UMAP can be used for dimensionality reduction, especially for non-linear data, and they often provide better visualization without losing interpretability.