local_coordinate_coding(1) local coordinate coding


local_coordinate_coding [-h] [-v] -k int -i string [-c string] [-d string] [-D string] [-l double] [-n int] [-N] [-o double] [-s int] -V


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 option. The l1-norm regularization parameter is specified with -l. For example, to run LCC on the dataset in data.csv using 200 atoms and an l1-regularization parameter of 0.1, saving the dictionary into dict.csv and the codes into codes.csv, use

$ local_coordinate_coding -i data.csv -k 200 -l 0.1 -d dict.csv -c codes.csv

The maximum number of iterations may be specified with the -n option. Optionally, the input data matrix X can be normalized before coding with the -N option.


--atoms (-k) [int]
Number of atoms in the dictionary.
--input_file (-i) [string]
Filename of the input data.


--codes_file (-c) [string]
Filename to save the output codes to. Default value 'codes.csv'.
--dictionary_file (-d) [string]
Filename to save the output dictionary to. Default value 'dictionary.csv'.
--help (-h)
Default help info.
--info [string]
Get help on a specific module or option. Default value ''.
--initial_dictionary (-D) [string]
Filename for optional initial dictionary. Default value ''.
--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)
If set, the input data matrix will be normalized before coding.
--objective_tolerance (-o) [double]
Tolerance for objective function. Default value 0.01.
--seed (-s) [int]
Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
--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.


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.