What is robust PCA and how does it differ from standard PCA?

Robust PCA separates low-rank structure from sparse noise explicitly.

Standard PCA minimizes squared reconstruction error and is sensitive to outliers. Robust PCA decomposes data into a low-rank matrix (signal) and a sparse matrix (outliers/noise), allowing it to recover underlying structure even with corruptions.