What is the trade-off between bias and variance in dimensionality reduction?

Lower dimensions increase bias but reduce variance, while higher dimensions reduce bias but increase variance.

Dimensionality reduction introduces bias by simplifying data representation. However, it reduces variance by eliminating noise and redundant features. Choosing dimensionality involves balancing information loss (bias) against model stability (variance), similar to classical machine learning trade-offs.