NAME

       mlpack_sparse_coding - sparse coding

SYNOPSIS

        mlpack_sparse_coding [-h] [-v]

DESCRIPTION

       An  implementation  of Sparse Coding with Dictionary Learning, which achieves sparsity via
       an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer  on  the  codes
       (the  Elastic  Net).  Given  a  dense data matrix X with n points and d dimensions, sparse
       coding seeks to find a dense dictionary matrix D with k  atoms  in  d  dimensions,  and  a
       sparse coding matrix Z with n points in k dimensions.

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

       Once  a  dictionary  D  is  found,  the  sparse  coding  model may be used to encode other
       matrices, and saved for future usage.

       To run this program, either an input matrix or an already-saved sparse coding  model  must
       be specified. An input matrix may be specified with the --training_file (-t) option, along
       with the number of atoms in the dictionary (--atoms,  or  -k).  It  is  also  possible  to
       specify  an  initial  dictionary  for the optimization, with the --initial_dictionary (-i)
       option. An input model may be specified with the --input_model_file (-m) option. There are
       also other training options available.

       As  an  example, to build a sparse coding model on the dataset in data.csv using 200 atoms
       and an l1-regularization parameter of 0.1, saving the model into model.xml, use

       $ sparse_coding -t data.csv -k 200 -l 0.1 -M model.xml

       Then, this model could be used to encode a new matrix, otherdata.csv, and save the  output
       codes to codes.csv:

       $ sparse_coding -m model.xml -T otherdata.csv -c codes.csv

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  sparse  coding  model.
              Default value ''.

       --lambda1 (-l) [double]
              Sparse coding l1-norm regularization parameter.  Default value 0.

       --lambda2 (-L) [double]
              Sparse coding l2-norm regularization parameter.  Default value 0.

       --max_iterations (-n) [int]
              Maximum number of iterations for sparse coding  (0  indicates  no  limit).  Default
              value  0.   --newton_tolerance  (-w)  [double]  Tolerance for convergence of Newton
              method.  Default value 1e-06.

       --normalize (-N)
              If   set,   the   input   data   matrix   will   be   normalized   before   coding.
              --objective_tolerance  (-o)  [double]  Tolerance  for  convergence of the objective
              function. Default value 0.01.

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

       --test_file (-T) [string]
              File containing data matrix to be encoded by trained model. 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_sparse_coding(16 November 2017)