wehub-resource-sync
33 lines
762 B
Matlab
Executable File
33 lines
762 B
Matlab
Executable File
function model = adaboostBin(X, t)
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% Adaboost for binary classification (weak learner: kmeans)
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% Input:
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% X: d x n data matrix
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% t: 1 x n label (0/1)
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% Output:
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% model: trained model structure
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% Written by Mo Chen (sth4nth@gmail.com).
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t = t+1;
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k = 2;
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[d,n] = size(X);
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w = ones(1,n)/n;
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M = 100;
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Alpha = zeros(1,M);
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Theta = zeros(d,k,M);
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T = sparse(1:n,t,1,n,k,n); % transform label into indicator matrix
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for m = 1:M
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% weak learner
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E = spdiags(w',0,n,n)*T;
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E = E*spdiags(1./sum(E,1)',0,k,k);
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c = X*E;
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[~,y] = min(sqdist(c,X),[],1);
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Theta(:,:,m) = c;
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% adaboost
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I = y~=t;
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e = dot(w,I);
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alpha = log((1-e)/e);
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w = w.*exp(alpha*I);
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w = w/sum(w);
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Alpha(m) = alpha;
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end
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model.alpha = Alpha;
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model.theta = Theta; |