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mlpack_local_coordinate_coding - local coordinate coding


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


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. 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.



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


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