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.