| local_coordinate_coding {mlpack} | R Documentation |
An implementation of Local Coordinate Coding (LCC), a data transformation technique. Given input data, this transforms each point to be expressed as a linear combination of a few points in the dataset; once an LCC model is trained, it can be used to transform points later also.
local_coordinate_coding( atoms = NA, initial_dictionary = NA, input_model = NA, lambda = NA, max_iterations = NA, normalize = FALSE, seed = NA, test = NA, tolerance = NA, training = NA, verbose = FALSE )
atoms |
Number of atoms in the dictionary. Default value "0" (integer). |
initial_dictionary |
Optional initial dictionary (numeric matrix). |
input_model |
Input LCC model (LocalCoordinateCoding). |
lambda |
Weighted l1-norm regularization parameter. Default value "0" (numeric). |
max_iterations |
Maximum number of iterations for LCC (0 indicates no limit). Default value "0" (integer). |
normalize |
If set, the input data matrix will be normalized before coding. Default value "FALSE" (logical). |
seed |
Random seed. If 0, 'std::time(NULL)' is used. Default value "0" (integer). |
test |
Test points to encode (numeric matrix). |
tolerance |
Tolerance for objective function. Default value "0.01" (numeric). |
training |
Matrix of training data (X) (numeric matrix). |
verbose |
Display informational messages and the full list of parameters and timers at the end of execution. Default value "FALSE" (logical). |
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" parameter. The l1-norm regularization parameter is specified with the "lambda" parameter.
A list with several components:
codes |
Output codes matrix (numeric matrix). |
dictionary |
Output dictionary matrix (numeric matrix). |
output_model |
Output for trained LCC model (LocalCoordinateCoding). |
mlpack developers
# For example, to run LCC on the dataset "data" using 200 atoms and an # l1-regularization parameter of 0.1, saving the dictionary "dictionary" and # the codes into "codes", use ## Not run: output <- local_coordinate_coding(training=data, atoms=200, lambda=0.1) dict <- output$dictionary codes <- output$codes ## End(Not run) # The maximum number of iterations may be specified with the "max_iterations" # parameter. Optionally, the input data matrix X can be normalized before # coding with the "normalize" parameter. # # An LCC model may be saved using the "output_model" output parameter. Then, # to encode new points from the dataset "points" with the previously saved # model "lcc_model", saving the new codes to "new_codes", the following # command can be used: ## Not run: output <- local_coordinate_coding(input_model=lcc_model, test=points) new_codes <- output$codes ## End(Not run)