Ubuntu Manpage: mlpack_sparse_coding

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

       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)