Man page - mlpack_local_coordinate_coding(1)
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Manual
mlpack_local_coordinate_coding
NAMESYNOPSIS
DESCRIPTION
OPTIONAL INPUT OPTIONS
OPTIONAL OUTPUT OPTIONS
ADDITIONAL INFORMATION
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.