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bbpca  Principal component analysis
   [SIG,MU,U,V]=bbpca(X,L), or [SIG,MU,U,V]=bbpca(X,L,'row'), computes
   the L leading principal components of the data-matrix X, assuming the
   objects are arranged as rows in X. X can be a matrix or black-box

   SIG is a vector of the singular values of the centered data-matrix.
   The eigenvalues of the covariance matrix is given by LAMBDA=SIG.^2.

   MU is the empirical mean of the objects, which is subtracted each
   object before the leading components are computed.

   U contains the eigenvectors of the empirical covariance matrix.
   The factor-scores matrix is given by U*diag(sig), while the
   factor loading matrix is given by V.

   The data-matrix can be written in terms of all principal components:
     X = U*diag(SIG)*V' + repmat(MU,size(X,1),1)

   [SIG,MU,U,V]=bbpca(X,L,'col') computes the PCA when the columns are
   arranged in columns. Assuming all principal components are computed,
   the data-matrix is given by:
     X = U*diag(SIG)*V' + repmat(MU,1,size(X,2))

   The factor-scores matrix is given by diag(sig)*V, and the
   factor-loading matrix is U.

   bbpca uses bbsvds for the computation. As a last argument you may
   pass a struct to control this computation.


   See also bbsvds, BBSVDSOPT.