1mlpack_pca(1)               General Commands Manual              mlpack_pca(1)
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NAME

6       mlpack_pca - principal components analysis
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SYNOPSIS

9        mlpack_pca [-h] [-v]
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DESCRIPTION

12       This  program  performs  principal  components  analysis  on  the given
13       dataset using the exact, randomized or QUIC SVD method. It will  trans‐
14       form  the  data  onto  its  principal components, optionally performing
15       dimensionality reduction by ignoring the principal components with  the
16       smallest eigenvalues.
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REQUIRED INPUT OPTIONS

19       --input_file (-i) [string]
20              Input dataset to perform PCA on.
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OPTIONAL INPUT OPTIONS

23       --decomposition_method  (-c) [string] Method used for the principalcom‐
24       ponents  analysis:  'exact',  'randomized',  'quic'.    Default   value
25       'exact'.
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27       --help (-h)
28              Default help info.
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30       --info [string]
31              Get  help  on  a  specific  module or option.  Default value ''.
32              --new_dimensionality (-d) [int] Desired dimensionality of output
33              dataset.   If  0,  no  dimensionality  reduction  is  performed.
34              Default value 0.
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36       --scale (-s)
37              If set, the data will be scaled before running  PCA,  such  that
38              the variance of each feature is
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40              1.
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42                  --var_to_retain  (-r) [double] Amount of variance to retain;
43                  should be between 0 and 1. If 1, all variance  is  retained.
44                  Overrides -d. Default value 0.
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46       --verbose (-v)
47              Display  informational  messages and the full list of parameters
48              and timers at the end of execution.
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50       --version (-V)
51              Display the version of mlpack.
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OPTIONAL OUTPUT OPTIONS

54       --output_file (-o) [string]
55              File to save modified dataset to. Default value ’'.
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ADDITIONAL INFORMATION

ADDITIONAL INFORMATION
59       For further information, including relevant papers, citations, and the‐
60       ory, For further information, including relevant papers, citations, and
61       theory, consult the documentation  found  at  http://www.mlpack.org  or
62       included    with    your    consult    the   documentation   found   at
63       http://www.mlpack.org or included with  your  DISTRIBUTION  OF  MLPACK.
64       DISTRIBUTION OF MLPACK.
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68                                                                 mlpack_pca(1)



        

    


            
        

        

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