What is the role of noise sensitivity in dimensionality reduction methods?

Different DR methods respond differently to noise depending on assumptions about data structure.

Linear methods like PCA tend to be robust to Gaussian noise because they focus on variance structure, but they can still be affected by outliers. Nonlinear methods like t-SNE may overfit noise in local neighborhoods. Robust DR methods explicitly model or downweight noise to preserve meaningful structure.