NAME

       mlpack_local_coordinate_coding - local coordinate coding

SYNOPSIS

        mlpack_local_coordinate_coding [-k int] [-i string] [-m unknown] [-l double] [-n int] [-N bool] [-s int] [-T string] [-o double] [-t string] [-V bool] [-c string] [-d string] [-M unknown] [-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_file
       (-i)'  parameter.  The  l1-norm regularization parameter is specified with the '--lambda (-l)' parameter.
       For example, to run LCC on the dataset 'data.csv' using 200 atoms and an l1-regularization  parameter  of
       0.1, saving the dictionary ’--dictionary_file (-d)' and the codes into '--codes_file (-c)', use

       $  mlpack_local_coordinate_coding  --training_file  data.csv  --atoms  200 --lambda 0.1 --dictionary_file
       dict.csv --codes_file codes.csv

       The maximum number of iterations may be specified with the '--max_iterations (-n)' parameter. Optionally,
       the input data matrix X can be normalized before coding with the '--normalize (-N)' parameter.

       An  LCC  model  may  be  saved using the '--output_model_file (-M)' output parameter. Then, to encode new
       points from the dataset 'points.csv' with the previously saved  model  'lcc_model.bin',  saving  the  new
       codes to ’new_codes.csv', the following command can be used:

       $  mlpack_local_coordinate_coding  --input_model_file  lcc_model.bin  --test_file points.csv --codes_file
       new_codes.csv

OPTIONAL INPUT OPTIONS

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

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --initial_dictionary_file (-i) [string]
              Optional initial dictionary.

       --input_model_file (-m) [unknown]
              Input LCC model.

       --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) [bool]
              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]
              Test points to encode.

       --tolerance (-o) [double]
              Tolerance for objective function. Default value 0.01.

       --training_file (-t) [string]
              Matrix of training data (X).

       --verbose (-v) [bool]
              Display informational messages and the full list of parameters and timers at the end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --codes_file (-c) [string]
              Output codes matrix.

       --dictionary_file (-d) [string]
              Output dictionary matrix.

       --output_model_file (-M) [unknown]
              Output for trained LCC model.

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  consult  the  documentation
       found at http://www.mlpack.org or included with your distribution of mlpack.