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  Singular Value Decomposition <i>versus</i> Principal Component Analysis  +  #REDIRECT [[Singular Value Decomposition versus Principal Component Analysis]] 
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  <i>SVD meets PCA</i>, by Cleve Moler
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  [https://www.mathworks.com/company/newsletters/articles/professorsvd.html MATLAB News & Notes, 2006, Cleve’s Corner]
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  <pre>“The Wikipedia pages on SVD and PCA are
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  quite good and contain a number of useful links,
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  although not to each other.”</pre>
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  <pre>
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  %relationship of pca to svd
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  m=3; n=7;
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  A = randn(m,n);
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  [coef,score,latent] = pca(A);
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  X = A  mean(A);
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  [U,S,V] = svd(X,'econ');
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  % U vs. score
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  rho = rank(X);
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  sense = sign(score).*sign(U*S(:,1:rho)); %account for negated left singular vector
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  sum(sum(abs(score  U*S(:,1:rho).*sense)))
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  % S vs. latent
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  sum(abs(latent  diag(S(:,1:rho)).^2/(m1)))
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  % V vs. coef
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  sense2 = sign(coef).*sign(V(:,1:rho)); %account for corresponding negated right singular vector
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  sum(sum(abs(coef  V(:,1:rho).*sense2)))
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  </pre>
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Current revision
 REDIRECT Singular Value Decomposition versus Principal Component Analysis