# Singular Value Decomposition versus Principal Component Analysis

### From Wikimization

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- | <i>coef, score, latent</i> definitions from | + | <i>coef, score, latent</i> definitions from |

- | [https://www.mathworks.com/help/stats/pca.html pca()] | + | [https://www.mathworks.com/help/stats/pca.html Matlab pca()] |

command. | command. | ||

## Revision as of 13:28, 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.