Provided by: mlpack-bin_2.2.5-1build1_amd64 
NAME
mlpack_pca - principal components analysis
SYNOPSIS
mlpack_pca [-h] [-v]
DESCRIPTION
This program performs principal components analysis on the given dataset using the exact, randomized 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.
REQUIRED INPUT OPTIONS
--input_file (-i) [string]
Input dataset to perform PCA on.
OPTIONAL INPUT OPTIONS
--decomposition_method (-c) [string] Method used for the principalcomponents analysis: 'exact',
'randomized', 'quic'. Default value 'exact'.
--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 (-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)
Display informational messages and the full list of parameters and timers at the end of execution.
--version (-V)
Display the version of mlpack.
OPTIONAL OUTPUT OPTIONS
--output_file (-o) [string]
File to save modified dataset to. Default value ’'.
ADDITIONAL INFORMATION
ADDITIONAL INFORMATION
For further information, including relevant papers, citations, and theory, For further information,
including relevant papers, citations, and theory, consult the documentation found at
http://www.mlpack.org or included with your consult the documentation found at http://www.mlpack.org or
included with your DISTRIBUTION OF MLPACK. DISTRIBUTION OF MLPACK.
mlpack_pca(16 November 2017)