SDPCA  Principal Component Analysis

    P=SDPCA(DATA,DIM)
    P=SDPCA(DATA,FRAC)
    P=SDPCA(DATA)

 INPUT
   DATA    SDDATA set or data matrix
   DIM     Output dimensionality
   FRAC    fraction of preserved variance (0,1)

 OUTPUT
   P       PCA projection

 DESCRIPTION
 SDPCA implements Principal Component Analysis projection maximizing
 variance in the data set DATA. SDPCA training is unsupervised meaning
 that the class labels are not used. If called without additional
 parameter, SDPCA projects data to a subspace with non-zero eigenvalues.

 READ MORE
 http://perclass.com/doc/latest/guide/dimensionality_reduction.html#sdpca

 SEE ALSO
 SDLDA

sdpca is referenced in examples:

• kb24: Example on building an image detector
• kb23: Example on image classification