# Singular Value Decomposition versus Principal Component Analysis

### From Wikimization

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- | from <i>SVD meets PCA</i> slide by Cleve Moler. | + | from [https://www.mathworks.com/videos/the-singular-value-decomposition-saves-the-universe-1481294462044.html <i>SVD meets PCA</i>] |

+ | slide [17:46] by Cleve Moler. | ||

“''The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.''” | “''The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.''” |

## Revision as of 13:27, 16 September 2018

from *SVD meets PCA*
slide [17:46] by Cleve Moler.

“*The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.*”

MATLAB News & Notes, Cleve’s Corner, 2006

%relationship of pca to svd m=3; n=7; A = randn(m,n); [coef,score,latent] = pca(A) X = A - mean(A); [U,S,V] = svd(X,'econ'); % S vs. latent rho = rank(X); latent = diag(S(:,1:rho)).^2/(m-1) % U vs. score sense = sign(score).*sign(U*S(:,1:rho)); %account for negated left singular vector score = U*S(:,1:rho).*sense % V vs. coef sense2 = sign(coef).*sign(V(:,1:rho)); %account for corresponding negated right singular vector coef = V(:,1:rho).*sense2

*coef, score, latent* definitions from Matlab
pca()
command.

Terminology like *variance* of principal components (PCs) can be found here:
Relationship between SVD and PCA.
*Standard deviation* is square root of variance.