AI Fundamentals

Dimension reduction (dimensionality reduction) refers to techniques—such as PCA, t-SNE, or autoencoders—that transform data with many features into a representation with fewer features while preserving the essential structure, variance, or relationships present in the original high-dimensional data. This is commonly used to reduce computational cost, mitigate the curse of dimensionality, remove noise, and enable visualization. Regression is a distinct supervised learning task focused on predicting continuous outcomes, and 'dimension enhancement' is not an established machine learning concept.