NAME

       mlpack_pca - principal components analysis

SYNOPSIS

        mlpack_pca -i string [-c string] [-d int] [-s bool] [-r double] [-V bool] [-o string] [-h -v]

DESCRIPTION

       This  program  performs  principal  components analysis on the given dataset using the exact, randomized,
       randomized block Krylov, or QUIC SVD method. It will transform the data onto  its  principal  components,
       optionally  performing  dimensionality  reduction  by ignoring the principal components with the smallest
       eigenvalues.

       Use the '--input_file (-i)'  parameter  to  specify  the  dataset  to  perform  PCA  on.  A  desired  new
       dimensionality  can  be specified with the ’--new_dimensionality (-d)' parameter, or the desired variance
       to retain can be specified with the '--var_to_retain (-r)' parameter. If  desired,  the  dataset  can  be
       scaled before running PCA with the '--scale (-s)' parameter.

       Multiple  different  decomposition  techniques  can  be used. The method to use can be specified with the
       '--decomposition_method (-c)' parameter, and it may take the values 'exact', 'randomized', or 'quic'.

       For example, to reduce the dimensionality of the matrix 'data.csv' to 5 dimensions using  randomized  SVD
       for the decomposition, storing the output matrix to 'data_mod.csv', the following command can be used:

       $ mlpack_pca --input_file data.csv --new_dimensionality 5 --decomposition_method randomized --output_file
       data_mod.csv

REQUIRED INPUT OPTIONS

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

OPTIONAL INPUT OPTIONS

       --decomposition_method (-c) [string]
              Method used for the  principal  components  analysis:  'exact',  'randomized',  'randomized-block-
              krylov', 'quic'. Default value 'exact'.

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

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

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

       --var_to_retain (-r) [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) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

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

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.