learn_combinatorial_graph_laplacian {spectralGraphTopology} | R Documentation |
Learn the Combinatorial Graph Laplacian from data
Learns a graph Laplacian matrix using the Combinatorial Graph Laplacian (CGL) algorithm proposed by Egilmez et. al. (2017)
learn_combinatorial_graph_laplacian(
S,
A_mask = NULL,
alpha = 0,
reltol = 1e-05,
max_cycle = 10000,
regtype = 1,
record_objective = FALSE,
verbose = TRUE
)
S |
sample covariance matrix |
A_mask |
binary adjacency matrix of the graph |
alpha |
L1-norm regularization hyperparameter |
reltol |
minimum relative error considered for the stopping criteri |
max_cycle |
maximum number of cycles |
regtype |
type of L1-norm regularization. If reg_type == 1, then all elements of the Laplacian matrix will be regularized. If reg_type == 2, only the off-diagonal elements will be regularized |
record_objective |
whether or not to record the objective function value at every iteration. Default is FALSE |
verbose |
if TRUE, then a progress bar will be displayed in the console. Default is TRUE |
A list containing possibly the following elements
laplacian |
estimated Laplacian Matrix |
elapsed_time |
elapsed time recorded at every iteration |
frod_norm |
relative Frobenius norm between consecutive estimates of the Laplacian matrix |
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 |
H. E. Egilmez, E. Pavez and A. Ortega, "Graph Learning From Data Under Laplacian and Structural Constraints", in IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 6, pp. 825-841, Sept. 2017. Original MATLAB source code is available at: https://github.com/STAC-USC/Graph_Learning