How does PCA handle high-dimensional sparse data?

Standard PCA struggles with sparse data; truncated SVD is preferred.

Sparse datasets (like text vectors) are inefficient for covariance computation. PCA requires dense matrix operations, while TruncatedSVD directly operates on sparse matrices without centering, making it scalable for large sparse systems.