In this paper, we are interested in learning the underlying graph structure behind training
data. Solving this basic problem is essential to carry out any graph signal processing or
machine learning task. To realize this, we assume that the data is smooth with respect to the
graph topology, and we parameterize the graph topology using an edge sampling function.
That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which
provides an explicit handle to control the sparsity level of the graph. We solve the sparse …

In this paper, we are interested in learning the underlying graph structure behind training
data. Solving this basic problem is essential to carry out any graph signal processing or
machine learning task. To realize this, we assume that the data is smooth with respect to the
graph topology, and we parameterize the graph topology using an edge sampling function.
That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which
provides an explicit handle to control the sparsity level of the graph. We solve the sparse …