mlpack_pca

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, 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.

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. Default value 0.

--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) [bool]

If set, the data will be scaled before running PCA, such that the variance of each feature is

1.

Default value 0. --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. Default value 0.

--version (-V) [bool]

Display the version of mlpack. Default value

0.

OPTIONAL OUTPUT OPTIONS

--output_file (-o) [string]

Matrix 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.