How do I visualize the SVM classification once I perform SVM training in Matlab?
So far, I have only trained the SVM with:
% Labels are -1 or 1
groundTruth = Ytrain;
d = xtrain;
model = svmtrain(groundTruth, d);
4 Answers
7
votes
If you are using LIBSVM, you can plot classification results:
% Labels are -1 or 1
groundTruth = Ytrain;
d = xtrain;
figure
% plot training data
hold on;
pos = find(groundTruth==1);
scatter(d(pos,1), d(pos,2), 'r')
pos = find(groundTruth==-1);
scatter(d(pos,1), d(pos,2), 'b')
% now plot support vectors
hold on;
sv = full(model.SVs);
plot(sv(:,1),sv(:,2),'ko');
% now plot decision area
[xi,yi] = meshgrid([min(d(:,1)):0.01:max(d(:,1))],[min(d(:,2)):0.01:max(d(:,2))]);
dd = [xi(:),yi(:)];
tic;[predicted_label, accuracy, decision_values] = svmpredict(zeros(size(dd,1),1), dd, model);toc
pos = find(predicted_label==1);
hold on;
redcolor = [1 0.8 0.8];
bluecolor = [0.8 0.8 1];
h1 = plot(dd(pos,1),dd(pos,2),'s','color',redcolor,'MarkerSize',10,'MarkerEdgeColor',redcolor,'MarkerFaceColor',redcolor);
pos = find(predicted_label==-1);
hold on;
h2 = plot(dd(pos,1),dd(pos,2),'s','color',bluecolor,'MarkerSize',10,'MarkerEdgeColor',bluecolor,'MarkerFaceColor',bluecolor);
uistack(h1, 'bottom');
uistack(h2, 'bottom');
2
votes
Assuming your data has more than two dimensions, you can perform a PCA, project the data to 2D, then assign them a color according to the output of your svm classifier (e.g., red for class A, blue for class B). This is quick to do and you will see if there is anything to visualize. However, data with a high dimensionality are not likely to be easily visualizable in 2D.
0
votes
Check out this svm-toy function similar to the one in LIBSVM. Obviously it only works for 2D binary classification
