NAME

       mlpack_pca - principal components analysis

SYNOPSIS

        mlpack_pca [-h] [-v] -i string -o string [-d int] [-s] [-V double] --version

DESCRIPTION

       This  program  performs  principal  components  analysis  on  the  given  dataset. It will
       transform the data onto its principal  components,  optionally  performing  dimensionality
       reduction by ignoring the principal components with the smallest eigenvalues.

REQUIRED OPTIONS

       --input_file (-i) [string]
              Input dataset to perform PCA on.

       --output_file (-o) [string]
              File to save modified dataset to.

OPTIONS

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.  Default value ''.

       --new_dimensionality (-d) [int]
              Desired  dimensionality  of  output  dataset.  If 0, no dimensionality reduction is
              performed.  Default value 0.

       --scale (-s)
              If set, the data will be scaled before running PCA, such that the variance of  each
              feature is 1.

       --var_to_retain (-V) [double]
              Amount  of  variance  to  retain;  should be between 0 and 1. If 1, all variance is
              retained.  Overrides -d. Default value 0.

       --verbose (-v)
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.  --version Display the version of mlpack.

ADDITIONAL INFORMATION

       For  further  information,  including  relevant papers, citations, and theory, consult the
       documentation found at http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.

                                                                                    mlpack_pca(1)