mlpack_sparse_coding - sparse coding

       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.

mlpack-4.5.1                                     29 January 2025                         mlpack_sparse_coding(1)

       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 d dimensions and n points, 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  Z  * D. 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 (specified with the '--atoms (-k)' parameter). It is also possible to specify  an  initial
       dictionary  for the optimization, with the ’--initial_dictionary_file (-i)' parameter. An input model may
       be specified with the '--input_model_file (-m)' parameter.

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

       $ mlpack_sparse_coding--training_file data.csv --atoms 200 --lambda1 0.1 --output_model_file model.bin

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

       $ mlpack_sparse_coding--input_model_file model.bin --test_file otherdata.csv --codes_file codes.csv

mlpack_sparse_coding - sparse coding

--atoms(-k)[int]
              Number of atoms in the dictionary. Default value 15.

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

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

       --initial_dictionary_file(-i)[unknown]
              Optional initial dictionary matrix.

       --input_model_file(-m)[unknown]
              File containing input sparse coding model.

       --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)[bool]
              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.

       --test_file(-T)[unknown]
              Optional matrix to be encoded by trained model.

       --training_file(-t)[unknown]
              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.

--codes_file(-c)[unknown]
              Matrix to save the output sparse codes of the test matrix (--test_file) to.

       --dictionary_file(-d)[unknown]
              Matrix to save the output dictionary to.

       --output_model_file(-M)[unknown]
              File to save trained sparse coding model to.

mlpack_sparse_coding [-kint] [-iunknown] [-munknown] [-ldouble] [-Ldouble] [-nint] [-wdouble] [-Nbool] [-odouble] [-sint] [-Tunknown] [-tunknown] [-Vbool] [-cunknown] [-dunknown] [-Munknown] [-h-v]