Truncated SVD is a matrix factorization technique that reduces data dimensionality by decomposing a matrix into U, Σ, and V components, then zeroing out smaller singular values to reconstruct an approximation. Applied to a 1024×1024 moon image, keeping just 32 of 1024 components achieves 16x data reduction while preserving recognizable image quality, demonstrating how leading components capture most information.