Loading…
Glossary term
Reducing the number of variables in data while keeping its important structure.
Real data can have hundreds or thousands of variables. Dimensionality reduction maps it to far fewer while keeping its structure, using methods such as PCA, t-SNE or UMAP.
It helps with visualization, speed and fighting the curse of dimensionality.
It lets people see and work with complex data.