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Nathanael Perraudin
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‎Contents.m

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% Url: http://lts2research.epfl.ch/gsp/doc/Contents.php
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% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
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% This file is part of GSPbox version 0.5.1
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‎demos/Contents.m

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% gsp_demo_wavelet - Introduction to spectral wavelets
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% gsp_demo_graph_embedding - Introduction to use embeddings
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%
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% Convex optimization on graph
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% Convex optimization on graphs
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% gsp_demo_graph_tv - Graph TV demonstration
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% gsp_demo_wavelet_dn - Graph wavelets denoising
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% gsp_demo_learn_graph - Graph learning demonstration
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%
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% Sparse approximation
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% gsp_demo_pyramid - Kron Pyramid applied to a signal
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% Url: http://lts2research.epfl.ch/gsp/doc/demos/Contents.php
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‎demos/gsp_demo.m

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% Url: http://lts2research.epfl.ch/gsp/doc/demos/gsp_demo.php
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‎demos/gsp_demo_graph_embedding.m

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% Url: http://lts2research.epfl.ch/gsp/doc/demos/gsp_demo_graph_embedding.php
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‎demos/gsp_demo_graph_tv.m

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% Url: http://lts2research.epfl.ch/gsp/doc/demos/gsp_demo_graph_tv.php
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‎demos/gsp_demo_learn_graph.m

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% subplot(2,2,4); scatter(xc, yc, 700, X(:,4), '.');
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% title('4th smooth signal'); axis off; colorbar;
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%
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% Figure 1: caption
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% Figure 1: Different signals
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%
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%
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%
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% We can compute the pairwise distances of the features and learn a graph
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% using them:
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%
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% Z1 = gsp_distanz(X(:, 1)').^2;
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% W1 = gsp_learn_graph_log_degrees(Z1, 1.5, 1, params);
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%
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% subplot(2,2,4); gsp_plot_edges(G4, params_plot);
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% title('graph learned from 4th smooth signal');
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% Figure 2: caption
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% Figure 2: Different graphs learned
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%
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% figure; gsp_plot_graph(G, params_plot);
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% title('Graph with edges learned from above 4 signals');
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% Figure 3: caption
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% Figure 3: Graph with edges learned from above 4 signals
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%
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%
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%
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% This is close to the graph that we would learn using the acutal
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% coordinates as features. So why does it work so well? We can see that
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% the pattern of the pairwise distances using these features is similar to
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% the one of the pairwise geometric distances between nodes:
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% the pattern of the pairwise distances using these features is similar
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% to the one of the pairwise geometric distances between nodes:
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%
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% figure;
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% subplot(1, 2, 1);
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% imagesc(gsp_distanz([xc, yc]'));
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% title('Pairwise distances computed from features');
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% Figure 4: caption
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% Figure 4: Geometric pairwise distances between nodes
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%
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%
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%
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% Figure 5: Pairwise distances computed from features
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%
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%
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%
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% The functions available for learning a graph are
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% GSP_LEARN_GRAPH_LOG_DEGREES and GSP_LEARN_GRAPH_L2_DEGREES.
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% The functions available for learning a graph are
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% GSP_LEARN_GRAPH_LOG_DEGREES and GSP_LEARN_GRAPH_L2_DEGREES.
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% References:
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‎demos/gsp_demo_pyramid.m

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‎demos/gsp_demo_wavelet.m

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‎demos/gsp_demo_wavelet_dn.m

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% Url: http://lts2research.epfl.ch/gsp/doc/demos/gsp_demo_wavelet_dn.php
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‎embedding/Contents.m

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‎embedding/gsp_compute_coordinates.m

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‎embedding/gsp_isomap.m

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‎embedding/gsp_laplacian_eigenmaps.m

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‎embedding/gsp_lle.m

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‎embedding/gsp_weight2distance.m

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‎filters/Contents.m

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‎filters/gsp_approx_filter.m

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‎filters/gsp_design_abspline.m

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‎filters/gsp_design_can_dual.m

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‎filters/gsp_design_expwin.m

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‎filters/gsp_design_half_cosine.m

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‎filters/gsp_design_heat.m

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‎filters/gsp_design_held.m

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‎filters/gsp_design_itersine.m

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‎filters/gsp_design_mexican_hat.m

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‎filters/gsp_design_meyer.m

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‎filters/gsp_design_papadakis.m

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‎filters/gsp_design_regular.m

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‎filters/gsp_design_simoncelli.m

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‎filters/gsp_design_simple_tf.m

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‎filters/gsp_design_smooth_indicator.m

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‎filters/gsp_design_warped_translates.m

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‎filters/gsp_evaluate_can_dual.m

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% it under the terms of the GNU General Public License as published by

‎filters/gsp_filter.m

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% Url: http://lts2research.epfl.ch/gsp/doc/filters/gsp_filter.php
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% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
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% This file is part of GSPbox version 0.5.1
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% This file is part of GSPbox version 0.5.2
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by

‎filters/gsp_filter_analysis.m

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% Url: http://lts2research.epfl.ch/gsp/doc/filters/gsp_filter_analysis.php
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% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
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% This file is part of GSPbox version 0.5.1
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% This file is part of GSPbox version 0.5.2
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%
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% This program is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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end
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for ii=1:Nf
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c((1:G.N)+G.N * (ii-1),:)= gsp_igft(G, ...
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repmat(fie(:,ii),1,Nv) ...
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repmat(conj(fie(:,ii)),1,Nv) ...
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.* gsp_gft(G, s));
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end
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