Umap, Machine Learning, AI, Dimensionality Reduction.
data science to combat the curse of dimensionality. Linear methods like PCA, Factor Analysis, ICA, and NMF are valuable when data exhibits clear linear relationships and interpretability is crucial. However, non-linear dimensionality reduction methods like Kernel PCA, UMAP, and t-SNE shine when