Truncated SVD

Automated excerpt

Singular Value Decomposition (SVD) is a matrix factorization technique that factors a real matrix M into three matrices U, Σ, and V such that M=U*Σ*V^T. This is very similar to PCA, excepting that the factorization for SVD is done on the data matrix, whereas for PCA, the factorization is done on the covariance matrix. Columns of U*Σ are principal components (scores). Singular values are related to the eigenvalues of the covariance matrix. The truncated factors are U' (1024x32), the 32 surviving singular values, and V' (1024x32).

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