Dimensionality Reduction
Dimension Reduction
In statistics, machine learning, and information theory, the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Origin
Karl Pearson laid the foundation in 1901 with his paper "On Lines and Planes of Closest Fit to Systems of Points in Space," published in the Philosophical Magazine — effectively inventing what would later be called principal component analysis (PCA). Harold Hotelling independently developed and named the technique in the 1930s. The broader umbrella term "dimensionality reduction" came into wide use as the field of machine learning grew in the late 20th century.
Everyday Use
Imagine choosing a restaurant based on 50 review criteria — overwhelming. But if you boil it down to "food quality, price, and location," you can decide quickly. Dimensionality reduction is the same trick applied to data: strip away the noise, keep the signal, and make complex information manageable.