xenial (1) mlpack_local_coordinate_coding.1.gz

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NAME

       mlpack_local_coordinate_coding - local coordinate coding

SYNOPSIS

        mlpack_local_coordinate_coding [-h] [-v] [-k int] [-c string] [-d string] [-i string] [-m string] [-l double] [-n int] [-N] [-M string] [-s int] [-T string] [-o double] [-t string] -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.

OPTIONS

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

       --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 ''.

       --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.  --output_model_file (-M) [string]
              File to save trained LCC model to. Default value ''.

       --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.

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(1)