adjacency_mat

<code>adjacency_mat</code> calculates the adjacency matrix of a Gaussian weighted graph based on the distance between points in \(\mathbb{R}^3\).

Provides the standard operations for signal processing on graphs:
graph Fourier transform, spectral graph wavelet transform,
visualization tools. It also implements a data driven method
for graph signal denoising/regression, for details see
De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>.
The package also provides an interface to the SuiteSparse Matrix Collection,
<https://sparse.tamu.edu/>, a large and widely used set of sparse matrix
benchmarks collected from a wide range of applications.

Fabien Navarro

gasper

Graph Signal Processing

Basile de Loynes

Baptiste Olivier

adjacency_mat function

<dl><dt>pts</dt>
<dd>Matrix representing the coordinates of N points in \(\mathbb{R}^3\). Each row should correspond to a point.</dd>
<dt>f</dt>
<dd>A scalar potential function. By default, the Gaussian potential \(\exp(-x^2/8)\) is used.</dd>
<dt>s</dt>
<dd>Numeric threshold used to sparsify the adjacency matrix. Any value below this threshold will be set to zero. Default is 0.</dd></dl>

Arguments

Compute the Adjacency Matrix of a Gaussian Weighted Graph — adjacency_mat

<dl>

<dt>pts</dt>
<dd>Matrix representing the coordinates of N points in \(\mathbb{R}^3\). Each row should correspond to a point.</dd>


<dt>f</dt>
<dd>A scalar potential function. By default, the Gaussian potential \(\exp(-x^2/8)\) is used.</dd>


<dt>s</dt>
<dd>Numeric threshold used to sparsify the adjacency matrix. Any value below this threshold will be set to zero. Default is 0.</dd>

</dl>

adjacency_mat: Compute the Adjacency Matrix of a Gaussian Weighted Graph

Description

Usage

Value

Arguments

Details

See Also

Examples