table of contents
mlpack_local_coordinate_coding(1) | User Commands | mlpack_local_coordinate_coding(1) |
NAME¶
mlpack_local_coordinate_coding - local coordinate coding
SYNOPSIS¶
mlpack_local_coordinate_coding [-k int] [-i unknown] [-m unknown] [-l double] [-n int] [-N bool] [-s int] [-T unknown] [-o double] [-t unknown] [-V bool] [-c unknown] [-d unknown] [-M unknown] [-h -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_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
OPTIONAL INPUT OPTIONS¶
- --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.
OPTIONAL OUTPUT OPTIONS¶
- --codes_file (-c) [unknown]
- Output codes matrix.
- --dictionary_file (-d) [unknown]
- Output dictionary matrix.
- --output_model_file (-M) [unknown]
- Output for trained LCC model.
ADDITIONAL INFORMATION¶
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.
23 September 2024 | mlpack-4.5.0 |