learn_smooth_graph {spectralGraphTopology}

Learn a graph from smooth signals This function learns a connected graph given an observed signal matrix using the method proposed by Kalofilias (2016).

Description

Learn a graph from smooth signals

This function learns a connected graph given an observed signal matrix using the method proposed by Kalofilias (2016).

Usage

learn_smooth_graph(
  X,
  alpha = 0.01,
  beta = 1e-04,
  step_size = 0.01,
  maxiter = 1000,
  tol = 1e-04
)

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

step_size

learning rate

maxiter

maximum number of iterations

tol

relative tolerance used as stopping criteria

References

V. Kalofolias, "How to learn a graph from smooth signals", in Proc. Int. Conf. Artif. Intell. Statist., 2016, pp. 920–929.