Curse of Dimensionality

Fact

With many dimensions, data gets sparse, and distance and computing get harder.

In Plain Words

Dim Curse is like looking for your keys in a mansion. After room 80, every couch looks equally guilty.

In vector search and clustering, too many dimensions make distances blur. Models with lots of features feel it too.

Related Concepts

• Dimensionality Reduction
• Vector Embedding
• Vector search
• Feature Selection

Dim. Reduction
Dim. Reduction is one common way to ease Dim Curse.

Embedding
When embeddings have too many dimensions, distance gets less useful.

Vector search
Dim Curse makes nearest-neighbor search harder and less reliable.

Feature Selection
Feature Selection removes useless features, so the space gets less sparse.