Singular Value Decomposition versus Principal Component Analysis
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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.