Dim. Reduction — Dimensionality Reduction

Fact

A way to squeeze high-dimensional data into a smaller, useful form.

In Plain Words

Dimensionality reduction is like packing a messy backpack into one lunchbox. You keep the homework, not the mystery crumbs.

It helps draw messy data as a simple picture. It can cut noise and make models run faster.

Related Concepts

• Curse of Dimensionality
• Principal Component Analysis
• Vector Embedding
• Feature Selection

Dim Curse
Dimensionality reduction helps with sparse data and heavy computing in high dimensions.

PCA
PCA is one of the most classic ways to reduce dimensions.

Embedding
Many embedding methods learn a more compact form of data.

Feature Selection
Dimensionality reduction mixes features into new ones. It does not just delete old ones.