Truncated SVD

Truncated SVD is highly useful for dimensionality reduction in large and sparse data structures. It is especially effective in text mining, recommendation systems, and high-dimensional sparse feature spaces. Like PCA, it produces a low-dimensional representation, but because it does not require centering, it is more compatible with sparse matrices. This makes it especially valuable in practical data science workflows.