sparse_coding_main.cpp File Reference

#include <mlpack/prereqs.hpp>
#include <mlpack/core/util/io.hpp>
#include <mlpack/core/util/mlpack_main.hpp>
#include "sparse_coding.hpp"

Include dependency graph for sparse_coding_main.cpp:

This graph shows which files directly or indirectly include this file:

Functions
	BINDING_NAME ("Sparse Coding")
	BINDING_SHORT_DESC ("An implementation of Sparse Coding with Dictionary Learning. Given a " "dataset, this will decompose the dataset into a sparse combination of a " "few dictionary elements, where the dictionary is learned during " "computation; a dictionary can be reused for future sparse coding of new " "points.")
	BINDING_LONG_DESC ("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 d dimensions and n points, 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 Z * D. 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." "\" "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" " "+PRINT_PARAM_STRING("training")+" option, along with the number of " "atoms in the dictionary (specified with the "+PRINT_PARAM_STRING("atoms")+" parameter). It is also possible to specify" " an initial dictionary for the optimization, with the "+PRINT_PARAM_STRING("initial_dictionary")+" parameter. An input model may" " be specified with the "+PRINT_PARAM_STRING("input_model")+" parameter.")
	BINDING_EXAMPLE ("As an example, to build a sparse coding model on the dataset "+PRINT_DATASET("data")+" using 200 atoms and an l1-regularization " "parameter of 0.1, saving the model into "+PRINT_MODEL("model")+", use " "\"+PRINT_CALL("sparse_coding", "training", "data", "atoms", 200, "lambda1", 0.1, "output_model", "model")+"\" "Then, this model could be used to encode a new matrix, "+PRINT_DATASET("otherdata")+", and save the output codes to "+PRINT_DATASET("codes")+": " "\"+PRINT_CALL("sparse_coding", "input_model", "model", "test", "otherdata", "codes", "codes"))
	BINDING_SEE_ALSO ("@local_coordinate_coding", "#local_coordinate_coding")
	BINDING_SEE_ALSO ("Sparse dictionary learning on Wikipedia", "https://en.wikipedia.org/wiki/Sparse_dictionary_learning")
	BINDING_SEE_ALSO ("Efficient sparse coding algorithms (pdf)", "http://papers.nips.cc/paper/2979-efficient-sparse-coding-" "algorithms.pdf")
	BINDING_SEE_ALSO ("Regularization and variable selection via the elastic net", "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.124.4696&" "rep=rep1&type=pdf")
	BINDING_SEE_ALSO ("mlpack::sparse_coding::SparseCoding C++ class documentation", "@doxygen/classmlpack_1_1sparse__coding_1_1SparseCoding.html")
	PARAM_MATRIX_IN ("training", "Matrix of training data (X).", "t")
	PARAM_INT_IN ("atoms", "Number of atoms in the dictionary.", "k", 15)
	PARAM_DOUBLE_IN ("lambda1", "Sparse coding l1-norm regularization parameter.", "l", 0)
	PARAM_DOUBLE_IN ("lambda2", "Sparse coding l2-norm regularization parameter.", "L", 0)
	PARAM_INT_IN ("max_iterations", "Maximum number of iterations for sparse coding " "(0 indicates no limit).", "n", 0)
	PARAM_MATRIX_IN ("initial_dictionary", "Optional initial dictionary matrix.", "i")
	PARAM_FLAG ("normalize", "If set, the input data matrix will be normalized " "before coding.", "N")
	PARAM_INT_IN ("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "s", 0)
	PARAM_DOUBLE_IN ("objective_tolerance", "Tolerance for convergence of the " "objective function.", "o", 0.01)
	PARAM_DOUBLE_IN ("newton_tolerance", "Tolerance for convergence of Newton " "method.", "w", 1e-6)
	PARAM_MODEL_IN (SparseCoding, "input_model", "File containing input sparse " "coding model.", "m")
	PARAM_MODEL_OUT (SparseCoding, "output_model", "File to save trained sparse " "coding model to.", "M")
	PARAM_MATRIX_OUT ("dictionary", "Matrix to save the output dictionary to.", "d")
	PARAM_MATRIX_OUT ("codes", "Matrix to save the output sparse codes of the test " "matrix (--test_file) to.", "c")
	PARAM_MATRIX_IN ("test", "Optional matrix to be encoded by trained model.", "T")

Detailed Description

Author

Nishant Mehta

Executable for Sparse Coding.

mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information.