xenial (1) mlpack_sparse_coding.1.gz

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NAME

       mlpack_sparse_coding - sparse coding

SYNOPSIS

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

OPTIONS

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

       --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 ''.

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

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

       --test_file (-T) [string]
              File  containing  data  matrix  to be encoded by trained model. Default value ''.  --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_sparse_coding(1)