AI, Dimensionality Reduction, Machine Learning, Python, Data Science.
efficiency, or handling of missing data. In summary, PCA is suitable for unsupervised dimensionality reduction, LDA is effective for supervised problems with a focus on class separability, and SVD is versatile, catering to various applications including collaborative filtering and matrix