/
run_3sources.m
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run_3sources.m
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%
% Run MLRSSC on 3-sources dataset. Parameters are optimized over NMI
% measure.
%
%-------------------------------------------------------
clear;
addpath(genpath(cd))
load 3-sources
X{1} = bbc;
X{2} = guardian;
X{3} = reuters;
k = 6;
num_views = 3;
num_iter = 100;
%% Linear kernel multi-view LRSSC
fprintf('\nPairwise multiview LRSSC\n');
opts.mu = 10^2;
lambda1 = 0.3;
lambda3 = 0.3;
opts.lambda = [lambda1 (1-lambda1) lambda3];
opts.noisy = true;
A = pairwise_MLRSSC(X, opts); % joint affinity matrix
[best.CA best.F best.P best.R best.nmi best.AR] = spectral_clustering(A, k, truth);
best
fprintf('\nCentroid multiview LRSSC\n');
opts.mu = 10^2;
lambda1 = 0.1;
lambda3 = 0.7;
opts.lambda = [lambda1 (1-lambda1) lambda3];
A = centroid_MLRSSC(X, opts); % joint affinity matrix
[best.CA best.F best.P best.R best.nmi best.AR] = spectral_clustering(A, k, truth);
best
%% Gaussian kernel multi-view LRSSC
opts.kernel = 'Gaussian';
opts.err_thr = 10^(-5);
for v=1:num_views
sigma(v) = opt_sigma(X{v});
end
opts.sigma = [50*sigma(1) 5*sigma(2) 10*sigma(3)];
fprintf('\nKernel pairwise multiview LRSSC\n');
opts.mu = 10^3;
lambda1 = 0.3;
lambda3 = 0.3;
opts.lambda = [lambda1 (1-lambda1) lambda3];
A = pairwise_MLRSSC(X, opts); % joint affinity matrix
[best.CA best.F best.P best.R best.nmi best.AR] = spectral_clustering(A, k, truth);
best
fprintf('\nKernel centroid multiview LRSSC\n');
opts.mu = 10^4;
lambda1 = 0.3;
lambda3 = 0.7;
opts.lambda = [lambda1 (1-lambda1) lambda3];
A = centroid_MLRSSC(X, opts); % joint affinity matrix
[best.CA best.F best.P best.R best.nmi best.AR] = spectral_clustering(A, k, truth);
best