NAME

       mlpack_local_coordinate_coding - local coordinate coding

SYNOPSIS

        mlpack_local_coordinate_coding [-h] [-v]

DESCRIPTION

       An  implementation  of  Local Coordinate Coding (LCC), which codes data that approximately
       lives on a manifold using a variation of l1-norm regularized sparse coding. Given a  dense
       data  matrix X with n points and d dimensions, LCC seeks to find a dense dictionary matrix
       D with k atoms in d dimensions, and a coding matrix Z  with  n  points  in  k  dimensions.
       Because of the regularization method used, the atoms in D should lie close to the manifold
       on which the data points lie.

       The original data matrix X can then be reconstructed as D *  Z.  Therefore,  this  program
       finds  a  representation of each point in X as a sparse linear combination of atoms in the
       dictionary D.

       The coding is found with an algorithm which alternates between a  dictionary  step,  which
       updates the dictionary D, and a coding step, which updates the coding matrix Z.

       To run this program, the input matrix X must be specified (with -i), along with the number
       of atoms in the dictionary (-k). An initial dictionary may  also  be  specified  with  the
       --initial_dictionary  option.  The  l1-norm regularization parameter is specified with -l.
       For  example,  to  run  LCC  on  the  dataset  in  data.csv  using  200   atoms   and   an
       l1-regularization parameter of 0.1, saving the dictionary into dict.csv and the codes into
       codes.csv, use

       $ local_coordinate_coding -i data.csv -k 200 -l 0.1 -d dict.csv -c codes.csv

       The maximum number of iterations may be specified with the  -n  option.   Optionally,  the
       input data matrix X can be normalized before coding with the -N option.

OPTIONAL INPUT OPTIONS

       --atoms (-k) [int]
              Number of atoms in the dictionary. Default value 0.

       --help (-h)
              Default help info.

       --info [string]
              Get  help  on a specific module or option.  Default value ''.  --initial_dictionary
              (-i)  [string]  Filename  for  optional  initial  dictionary.   Default  value  ''.
              --input_model_file (-m) [string] File containing input LCC model. Default value ’'.

       --lambda (-l) [double]
              Weighted l1-norm regularization parameter.  Default value 0.

       --max_iterations (-n) [int]
              Maximum number of iterations for LCC (0 indicates no limit). Default value 0.

       --normalize (-N)
              If set, the input data matrix will be normalized before coding.

       --seed (-s) [int]
              Random seed. If 0, 'std::time(NULL)' is used.  Default value 0.

       --test_file (-T) [string]
              File of test points to encode. Default value ’'.

       --tolerance (-o) [double]
              Tolerance  for  objective  function.  Default  value  0.01.   --training_file  (-t)
              [string] Filename of the training data (X). Default value ''.

       --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

       --codes_file (-c) [string]
              Filename  to  save  the  output codes to. Default value ''.  --dictionary_file (-d)
              [string]  Filename  to  save  the  output  dictionary  to.    Default   value   ''.
              --output_model_file  (-M) [string] File to save trained LCC model 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_local_coordinate_coding(16 November 2017)