## bbpca

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the text-formatted help.

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
operator.
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.
Example:
[sig,mu,U,V]=bbpca(X,100);
See also bbsvds, BBSVDSOPT.

bbparcelbbpeek