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mlpack_sparse_coding - sparse coding


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


An implementation of Sparse Coding with Dictionary Learning, which achieves sparsity via
an l1-norm regularizer on the codes (LASSO) or an (l1+l2)-norm regularizer on the codes
(the Elastic Net). Given a dense data matrix X with n points and d dimensions, sparse
coding seeks to find a dense dictionary matrix D with k atoms in d dimensions, and a
sparse coding matrix Z with n points in k dimensions.

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 sparse coding is found with an algorithm which alternates between a dictionary step,
which updates the dictionary D, and a sparse coding step, which updates the sparse coding

Once a dictionary D is found, the sparse coding model may be used to encode other
matrices, and saved for future usage.

To run this program, either an input matrix or an already-saved sparse coding model must
be specified. An input matrix may be specified with the --training_file (-t) option, along
with the number of atoms in the dictionary (--atoms, or -k). It is also possible to
specify an initial dictionary for the optimization, with the --initial_dictionary (-i)
option. An input model may be specified with the --input_model_file (-m) option. There are
also other training options available.

As an example, to build a sparse coding model on the dataset in data.csv using 200 atoms
and an l1-regularization parameter of 0.1, saving the model into model.xml, use

$ sparse_coding -t data.csv -k 200 -l 0.1 -M model.xml

Then, this model could be used to encode a new matrix, otherdata.csv, and save the output
codes to codes.csv:

$ sparse_coding -m model.xml -T otherdata.csv -c codes.csv


--atoms (-k) [int]
Number of atoms in the dictionary. Default value 0.

--codes_file (-c) [string]
Filename to save the output sparse 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 sparse coding model.
Default value ''.

--lambda1 (-l) [double]
Sparse coding l1-norm regularization parameter. Default value 0.

--lambda2 (-L) [double]
Sparse coding l2-norm regularization parameter. Default value 0.

--max_iterations (-n) [int]
Maximum number of iterations for sparse coding (0 indicates no limit). Default
value 0. --newton_tolerance (-w) [double] Tolerance for convergence of Newton
method. Default value 1e-06.

--normalize (-N)
If set, the input data matrix will be normalized before coding.
--objective_tolerance (-o) [double] Tolerance for convergence of the objective
function. Default value 0.01. --output_model_file (-M) [string] File to save
trained sparse coding model to. Default value ''.

--seed (-s) [int]
Random seed. If 0, 'std::time(NULL)' is used. Default value 0.

--test_file (-T) [string]
File containing data matrix to be encoded by trained model. Default value ''.
--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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