Dimensionality Reduction Techniques in R

When we talk about dimensionality reduction in R, we're essentially referring to the process of converting a dataset with a large number of variables into a dataset with fewer variables. This process retains the significant information or features of the original dataset while reducing the computational complexity and helping in visualizing the data effectively. Three techniques that are widely used for this purpose are PCA (Principal Component Analysis), t-SNE (t-Distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection). Starting with PCA, it's a linear technique that identifies the directions, or principal components, that maximize the variance in the data...