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(New page: Singular Value Decomposition <i>versus</i> Principal Component Analysis SVD meets PCA MATLAB News & Notes, 2006, Cleve’s Corner <pre>“The Wikipedia pages on SVD and PCA are quite ...) |
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Singular Value Decomposition <i>versus</i> Principal Component Analysis | Singular Value Decomposition <i>versus</i> Principal Component Analysis | ||
| - | SVD meets PCA | + | <i>SVD meets PCA</i>, by Cleve Moler |
| - | MATLAB News & Notes, | + | [https://www.mathworks.com/company/newsletters/articles/professor-svd.html MATLAB News & Notes, 2006, Cleve’s Corner] |
| - | 2006, | + | |
| - | Cleve’s Corner | + | |
<pre>“The Wikipedia pages on SVD and PCA are | <pre>“The Wikipedia pages on SVD and PCA are | ||
quite good and contain a number of useful links, | quite good and contain a number of useful links, | ||
although not to each other.”</pre> | although not to each other.”</pre> | ||
| - | |||
| - | <math>-</math>https://www.mathworks.com/company/newsletters/articles/professor-svd.html | ||
<pre> | <pre> | ||
Revision as of 18:04, 17 October 2017
Singular Value Decomposition versus Principal Component Analysis
SVD meets PCA, by Cleve Moler
MATLAB News & Notes, 2006, Cleve’s Corner
“The Wikipedia pages on SVD and PCA are quite good and contain a number of useful links, although not to each other.”
%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'); % U vs. score rho = rank(X); sense = sign(score).*sign(U*S(:,1:rho)); %account for negated left singular vector sum(sum(abs(score - U*S(:,1:rho).*sense))) % S vs. latent sum(abs(latent - diag(S(:,1:rho)).^2/(m-1))) % V vs. coef sense2 = sign(coef).*sign(V(:,1:rho)); %account for corresponding negated right singular vector sum(sum(abs(coef - V(:,1:rho).*sense2)))