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function selectedFeatures = mRMR_D(k, featureMatrix, classColumn)
%function selectedFeatures = mRMR_D(k, featureMatrix, classColumn)
%
%Selects optimal features according to the mRMR-D algorithm from
%"Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy"
%by H. Peng et al. (2005)
%
%Calculates the top k features
%a dataset featureMatrix with n training examples and m features
%with the classes held in classColumn (an n x 1 vector)
noOfTraining = size(classColumn,1);
noOfFeatures = size(featureMatrix,2);
unselectedFeatures = ones(noOfFeatures,1);
classMI = zeros(noOfFeatures,1);
answerFeatures = zeros(k,1);
highestMI = 0;
highestMICounter = 0;
currentHighestFeature = 0;
featureMIMatrix = -(ones(k,noOfFeatures));
%setup the mi against the class
for n = 1 : noOfFeatures
classMI(n) = mi(featureMatrix(:,n),classColumn);
if classMI(n) > highestMI
highestMI = classMI(n);
highestMICounter = n;
end
end
answerFeatures(1) = highestMICounter;
unselectedFeatures(highestMICounter) = 0;
%iterate over the number of features to select
for i = 2:k
score = -100;
currentHighestFeature = 0;
iMinus = i-1;
for j = 1 : noOfFeatures
if unselectedFeatures(j) == 1
currentMIScore = 0;
for m = 1 : iMinus
if featureMIMatrix(m,j) == -1
featureMIMatrix(m,j) = mi(featureMatrix(:,j),featureMatrix(:,answerFeatures(m)));
end
currentMIScore = currentMIScore + featureMIMatrix(m,j);
end
currentScore = classMI(j) - (currentMIScore/iMinus);
if (currentScore > score)
score = currentScore;
currentHighestFeature = j;
end
end
end
if score < 0
disp(['at selection ' int2str(j) ' mRMRD is negative with value ' num2str(score)]);
end
%now highest feature is selected in currentHighestFeature
%store it
unselectedFeatures(currentHighestFeature) = 0;
answerFeatures(i) = currentHighestFeature;
end
selectedFeatures = answerFeatures;
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