1mlpack_pca(1)                    User Commands                   mlpack_pca(1)
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NAME

6       mlpack_pca - principal components analysis
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SYNOPSIS

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

12       This  program  performs  principal  components  analysis  on  the given
13       dataset using the exact, randomized, randomized block Krylov,  or  QUIC
14       SVD  method.  It will transform the data onto its principal components,
15       optionally performing dimensionality reduction by ignoring the  princi‐
16       pal components with the smallest eigenvalues.
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18       Use the '--input_file (-i)' parameter to specify the dataset to perform
19       PCA on.  A  desired  new  dimensionality  can  be  specified  with  the
20       ’--new_dimensionality  (-d)'  parameter,  or  the  desired  variance to
21       retain can be specified with the '--var_to_retain (-r)'  parameter.  If
22       desired, the dataset can be scaled before running PCA with the '--scale
23       (-s)' parameter.
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25       Multiple different decomposition techniques can be used. The method  to
26       use  can be specified with the '--decomposition_method (-c)' parameter,
27       and it may take the values 'exact', 'randomized', or 'quic'.
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29       For example, to reduce the dimensionality of the matrix 'data.csv' to 5
30       dimensions using randomized SVD for the decomposition, storing the out‐
31       put matrix to 'data_mod.csv', the following command can be used:
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33       $  pca  --input_file  data.csv  --new_dimensionality   5   --decomposi‐
34       tion_method randomized --output_file data_mod.csv
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REQUIRED INPUT OPTIONS

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

41       --decomposition_method (-c) [string]
42              Method  used  for  the  principal  components analysis: 'exact',
43              'randomized', 'randomized-block-krylov', 'quic'.  Default  value
44              'exact'.
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46       --help (-h) [bool]
47              Default help info.
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49       --info [string]
50              Get help on a specific module or option.  Default value ''.
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52       --new_dimensionality (-d) [int]
53              Desired  dimensionality of output dataset. If 0, no dimensional‐
54              ity reduction is performed.  Default value 0.
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56       --scale (-s) [bool]
57              If set, the data will be scaled before running  PCA,  such  that
58              the variance of each feature is 1.
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60       --var_to_retain (-r) [double]
61              Amount  of  variance to retain; should be between 0 and 1. If 1,
62              all variance is retained.  Overrides -d. Default value 0.
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64       --verbose (-v) [bool]
65              Display informational messages and the full list  of  parameters
66              and timers at the end of execution.
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68       --version (-V) [bool]
69              Display the version of mlpack.
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OPTIONAL OUTPUT OPTIONS

72       --output_file (-o) [string]
73              Matrix to save modified dataset to. Default value ''.
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ADDITIONAL INFORMATION

76       For further information, including relevant papers, citations, and the‐
77       ory,  consult  the  documentation  found  at  http://www.mlpack.org  or
78       included with your distribution of mlpack.
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82mlpack-3.0.4                   21 February 2019                  mlpack_pca(1)