learn_smooth_graph: Learn a graph from smooth signals This function learns a...

In spectralGraphTopology: Learning Graphs from Data via Spectral Constraints

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.

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