mlpack_sparse_coding(1) - Linux man page

Name

sparse_coding - sparse coding

Synopsis

 sparse_coding [-h] [-v] -k int -i string [-c string] [-d string] [-D string] [-l double] [-L double] [-n int] [-w double] [-N] [-o double] [-s int]

Description

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 matrix.

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 and l2 norm regularization parameters may be specified with -l and -L, respectively. For example, to run sparse coding 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

$ sparse_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.

Required Options

--atoms (-k) [int]

Number of atoms in the dictionary.

--input_file (-i) [string]

Filename of the input data.

Options

--codes_file (-c) [string]

Filename to save the output sparse codes to. Default value 'codes.csv'.

--dictionary_file (-d) [string]

Filename to save the output dictionary to. Default value 'dictionary.csv'.

--help (-h)

Default help info.

--info [string]

Get help on a specific module or option. Default value ''.

--initial_dictionary (-D) [string]

Filename for optional initial dictionary. 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.

--seed (-s) [int]

Random seed. If 0, 'std::time(NULL)' is used. Default value 0.

--verbose (-v)

Display informational messages and the full list of parameters and timers at the end of execution.

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.