Code covered by the BSD License
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A=matrix_normalizer(B)
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BMP(A, y, group, err)
Block Matching Pursuit
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BOMP(A, y, group, err)
Block Orthogonal Matching Pursuit - selection of group based on highest
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GMP(A, y, group, err)
Group Matching Pursuit
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GOMP(A, y, group, err)
Group orthogonal Matching Pursuit - selection of group based on highest
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GenGroupSparseProblem(m, n, n...
Initialize random number generator
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ReGOMP(A, y, group, sparsity,...
Regularized Group Orthogonal Matching Pursuit - Combining ideas from [1]
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StGOMP(A, y, group, steps, er...
Stagewise Group Orthogonal Matching Pursuit - Combining Ideas from [1]
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[s, err_mse, iter_time]=block...
block_gp: Block Gradient Pursuit algorithm (modification from [1])
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[s, err_mse, iter_time]=block...
group_nomp: Block Nearly Orthogonal Matching Pursuit algorithm
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[s, err_mse, iter_time]=block...
group_pcgp: Block Partial Conjugate Gradient Pursuit algorithm
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[s, err_mse, iter_time]=group...
group_gp: Group Gradient Pursuit algorithm (modification from [1])
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[s, err_mse, iter_time]=group...
group_nomp: Group Nearly Orthogonal Matching Pursuit algorithm
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[s, err_mse, iter_time]=group...
group_pcgp: Group Partial Conjugate Gradient Pursuit algorithm
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fdrthresh(z,q)
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demo.m
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View all files
from
Greedy Algorithms promoting Group Sparsity V2
by Angshul Majumdar
Approximate Greedy Solutions to the problem min||x(k)||_2,0 such that Ax = b
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| GOMP(A, y, group, err)
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function [s, residual] = GOMP(A, y, group, err)
% Group orthogonal Matching Pursuit - selection of group based on highest
% average correlation of each group
% Input
% A = N X d dimensional measurment matrix
% y = N dimensional observation vector
% group = labels
% Output
% s = estimated sparse signal
% residual = residual
% Copyright (c) Angshul Majumdar 2009
if nargin < 5
err = 1e-5;
end
c = max(group);
s = zeros(size(A,2),1); t = 2.5;
r(:,1) = y; L = []; Psi = [];
i = 2;
for j = 1:c
g{j} = find(group == j);
end
while (i < c) && (norm(r(:,end))>err)
l = A'*r(:,i-1);
for j = 1:c
lg(j) = mean(abs(l(g{j})));
end
[B, IX] = sort(lg, 'descend');
L = [L' g{IX(1)}']';
Psi = A(:,L);
x = Psi\y;
r(:,i) = y - Psi*x;
i = i+1;
end
s(L) = x;
residual = r(:,end);
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