gsp_demo_learn_graph_parameters.m
2cb18aae5832master

gsp_demo_learn_graph_parameters.m
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			n = 200;

			%%
			%
			% Url: http://lts2research.epfl.ch/gsp/doc/demos/gsp_demo_learn_graph_parameters.php

			% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
			% This file is part of GSPbox version 0.7.0
			%
			% This program is free software: you can redistribute it and/or modify
			% it under the terms of the GNU General Public License as published by
			% the Free Software Foundation, either version 3 of the License, or
			% (at your option) any later version.
			%
			% This program is distributed in the hope that it will be useful,
			% but WITHOUT ANY WARRANTY; without even the implied warranty of
			% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
			% GNU General Public License for more details.
			%
			% You should have received a copy of the GNU General Public License
			% along with this program. If not, see <http://www.gnu.org/licenses/>.

			% If you use this toolbox please kindly cite
			% N. Perraudin, J. Paratte, D. Shuman, V. Kalofolias, P. Vandergheynst,
			% and D. K. Hammond. GSPBOX: A toolbox for signal processing on graphs.
			% ArXiv e-prints, Aug. 2014.
			% http://arxiv.org/abs/1408.5781
			X = 5 * rand(2, n);
			Z = gsp_distanz(X).^2;

			%% first way to control sparsity (Kalofolias 2016)
			% this is easy to use when we solve an optimization problem that alternates
			% between graph learning and solving w.r.t. X with fixed graph

			alpha = .6; % bigger weights
			beta = .5; % smaller big weights (more dense)
			W1 = gsp_learn_graph_log_degrees(Z, alpha, beta);

			%% second way to control sparsity (Kalofolias, Vandergheynst 2017)
			% this is easy to use for controling sparsity. For a sparser graph just use
			% a bigger theta. Also in general converges faster with the default
			% stepsize.
			theta = 1/sqrt(alpha*beta); % controls sparsity
			delta = sqrt(alpha/beta); % controls magnitude
			W2 = delta * gsp_learn_graph_log_degrees(theta * Z, 1, 1);

			%max(W2(:))
			%max(W1(:))

			%% the two ways are equivalent:
			% Clean numerical error
			W1(W1<1e-4)=0;
			W2(W2<1e-4)=0;
			fprintf('Relative error between W1 and W2: %e\n', norm(W1-W2, 'fro')/norm(W1, 'fro'));

			%% Plot graph
			% figure; spy(W1); title('Learned adjacency matrix');
			% figure; spy(W2)
			figure; gsp_plot_graph(gsp_graph(W1, X')); title('Learned graph');

iCAPs/packages/gspbox/demos/gsp_demo_learn_graph_parameters.m2cb18aae5832master

gsp_demo_learn_graph_parameters.mView Options

iCAPs/packages/gspbox/demos/gsp_demo_learn_graph_parameters.m
2cb18aae5832master

gsp_demo_learn_graph_parameters.m
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