1mlpack_sparse_coding(1) General Commands Manual mlpack_sparse_coding(1)
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6 mlpack_sparse_coding - sparse coding
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9 mlpack_sparse_coding [-h] [-v]
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12 An implementation of Sparse Coding with Dictionary Learning, which
13 achieves sparsity via an l1-norm regularizer on the codes (LASSO) or an
14 (l1+l2)-norm regularizer on the codes (the Elastic Net). Given a dense
15 data matrix X with n points and d dimensions, sparse coding seeks to
16 find a dense dictionary matrix D with k atoms in d dimensions, and a
17 sparse coding matrix Z with n points in k dimensions.
18 The original data matrix X can then be reconstructed as D * Z. There‐
20 fore, this program finds a representation of each point in X as a
21 sparse linear combination of atoms in the dictionary D.
22 The sparse coding is found with an algorithm which alternates between a
24 dictionary step, which updates the dictionary D, and a sparse coding
25 step, which updates the sparse coding matrix.
26 Once a dictionary D is found, the sparse coding model may be used to
28 encode other matrices, and saved for future usage.
29 To run this program, either an input matrix or an already-saved sparse
31 coding model must be specified. An input matrix may be specified with
32 the --training_file (-t) option, along with the number of atoms in the
33 dictionary (--atoms, or -k). It is also possible to specify an initial
34 dictionary for the optimization, with the --initial_dictionary (-i)
35 option. An input model may be specified with the --input_model_file
36 (-m) option. There are also other training options available.
37 As an example, to build a sparse coding model on the dataset in
39 data.csv using 200 atoms and an l1-regularization parameter of 0.1,
40 saving the model into model.xml, use
41 $ sparse_coding -t data.csv -k 200 -l 0.1 -M model.xml
43 Then, this model could be used to encode a new matrix, otherdata.csv,
45 and save the output codes to codes.csv:
46 $ sparse_coding -m model.xml -T otherdata.csv -c codes.csv
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50 --atoms (-k) [int]
51 Number of atoms in the dictionary. Default value 0.
52 --help (-h)
54 Default help info.
55 --info [string]
57 Get help on a specific module or option. Default value ''.
58 --initial_dictionary (-i) [string] Filename for optional initial
59 dictionary. Default value ''. --input_model_file (-m) [string]
60 File containing input sparse coding model. Default value ''.
61 --lambda1 (-l) [double]
63 Sparse coding l1-norm regularization parameter. Default value
64 0.
65 --lambda2 (-L) [double]
67 Sparse coding l2-norm regularization parameter. Default value
68 0.
69 --max_iterations (-n) [int]
71 Maximum number of iterations for sparse coding (0 indicates no
72 limit). Default value 0. --newton_tolerance (-w) [double] Tol‐
73 erance for convergence of Newton method. Default value 1e-06.
74 --normalize (-N)
76 If set, the input data matrix will be normalized before coding.
77 --objective_tolerance (-o) [double] Tolerance for convergence of
78 the objective function. Default value 0.01.
79 --seed (-s) [int]
81 Random seed. If 0, 'std::time(NULL)' is used. Default value 0.
82 --training_file (-t) [string] Filename of the training data (X).
83 Default value ''.
84 --verbose (-v)
86 Display informational messages and the full list of parameters
87 and timers at the end of execution.
88 --version (-V)
90 Display the version of mlpack.
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93 --codes_file (-c) [string]
94 Filename to save the output sparse codes to. Default value ''.
95 --dictionary_file (-d) [string] Filename to save the output dic‐
96 tionary to. Default value ''. --output_model_file (-M)
97 [string] File to save trained sparse coding model to. Default
98 value ''.
99 --test_file (-T) [string]
101 File containing data matrix to be encoded by trained model.
102 Default value ''.
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106 For further information, including relevant papers, citations, and the‐
107 ory, For further information, including relevant papers, citations, and
108 theory, consult the documentation found at http://www.mlpack.org or
109 included with your consult the documentation found at
110 http://www.mlpack.org or included with your DISTRIBUTION OF MLPACK.
111 DISTRIBUTION OF MLPACK.
112 mlpack_sparse_coding(1)