learn_graph_sigrep: Learn graphs from a smooth signal representation approach...

In spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

Learn graphs from a smooth signal representation approach This function learns a graph from a observed data matrix using the method proposed by Dong (2016).

Description

Learn graphs from a smooth signal representation approach

This function learns a graph from a observed data matrix using the method proposed by Dong (2016).

Usage

learn_graph_sigrep(
  X,
  alpha = 0.001,
  beta = 0.5,
  maxiter = 1000,
  ftol = 1e-04,
  verbose = TRUE
)

Arguments

X	a p-by-n data matrix, where p is the number of nodes and n is the number of observations
alpha	hyperparameter that controls the importance of the Dirichlet energy penalty
beta	hyperparameter that controls the importance of the L2-norm regularization
maxiter	maximum number of iterations
ftol	relative error on the objective function to be used as the stopping criteria
verbose	if TRUE, then a progress bar will be displayed in the console. Default is TRUE

Value

A list containing the following items

laplacian	estimated Laplacian Matrix
Y	a smoothed approximation of the data matrix X
convergence	whether or not the algorithm has converged within the tolerance and max number of iterations
obj_fun	objective function value at every iteration, in case record_objective = TRUE

References

X. Dong, D. Thanou, P. Frossard and P. Vandergheynst, "Learning Laplacian Matrix in Smooth Graph Signal Representations," in IEEE Transactions on Signal Processing, vol. 64, no. 23, pp. 6160-6173, Dec.1, 2016.

spectralGraphTopology documentation built on March 18, 2022, 7:35 p.m.