mlpack_local_coordinate_coding - local coordinate 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_local_coordinate_coding(1)

       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

mlpack_local_coordinate_coding - local coordinate coding

--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)[unknown]
              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)[unknown]
              Test points to encode.

       --tolerance(-o)[double]
              Tolerance for objective function. Default value 0.01.

       --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]
              Output codes matrix.

       --dictionary_file(-d)[unknown]
              Output dictionary matrix.

       --output_model_file(-M)[unknown]
              Output for trained LCC model.

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