mlpack_local_coordinate_coding man page

mlpack_local_coordinate_coding — local coordinate coding

Synopsis

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

Description

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.

Options

--atoms (-k) [int]
Number of atoms in the dictionary. Default value 0.
--codes_file (-c) [string]
Filename to save the output 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 LCC model. 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. --output_model_file (-M) [string] File to save trained LCC model to. Default value ''.
--seed (-s) [int]
Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
--test_file (-T) [string]
File of test points to encode. Default value ’'.
--tolerance (-o) [double]
Tolerance for objective function. Default value 0.01. --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.

Info