Introduction to dimensionality reduction

Dimensionality reduction is a technique used to simplify complex datasets by reducing the number of dimensions (columns) while preserving important information. This helps make working with large, intricate data more manageable. The process can be likened to casting a shadow of an object - some detail is lost, but it becomes easier to manipulate and compare. Dimensionality reduction techniques are crucial in handling high-dimensional datasets where traditional methods may fail due to the curse of dimensionality. These techniques come in two forms: linear and non-linear. Linear techniques maintain linear relationships in data while reducing dimensions, while non-linear ones capture more complex, non-linear relationships. Examples include Principal Component Analysis (PCA), Independent Component Analysis (ICA) for linear methods, and UMAP, t-SNE, Multidimensional Scaling for non-linear methods.