mlpack_pca man page

mlpack_pca — principal components analysis


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


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.  


--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 or included with your DISTRIBUTION OF MLPACK.