How does PCA behave under noisy data conditions?

PCA reduces noise by discarding low-variance components but may also lose weak signals.

Noise often manifests as random variance in small directions. PCA captures dominant variance directions and allows removal of minor components, effectively filtering noise. However, if signal is weak, it may be mistakenly removed as noise.