为什么predicted_label +1 即使它应该是-1？ 在 MATLAB 中使用 LIBSVM

Question

I extracted the principal components of training and testing data.
'trainingdata.train' has feature values from both +1(face 1) and -1(all other faces) labels. 'testdata.train' has feature values from face 2 and no label since i want the SVM to predict its label. The "predicted_label" given by LIBSVM is +1 even though it should be -1.

[training_label_matrix, training_instance_matrix] = libsvmread('trainingdata.train');
[testing_label_matrix, testing_instance_matrix] = libsvmread('testdata.train');
model = svmtrain(training_label_matrix, training_instance_matrix);
[predicted_label] = svmpredict(testing_label_matrix, testing_instance_matrix, model);

Please point me out to what i am doing wrong.

Answer 1

Use [predict_label, accuracy, prob_values] = svmpredict(testLabel, testData, model, '-b 1'); to observe the accuracy.

testLabel is the vector that includes the 'correct' labels of your test data. This parameter is given in order to calculate the accuracy . In the real case that labels of test data are unknown, simply use any random values to get the predict_label without calculating the accuracy .

Besides, although not required, you'd better specify the options in svmtrain , check their page for more details.

Answer 2

@Lennon : So should the code go like this?

[training_label_matrix, training_instance_matrix] = libsvmread('trainingdata.train');
[testing_label_matrix, testing_instance_matrix] = libsvmread('testdata.train');
model = svmtrain(training_label_matrix, training_instance_matrix);
[predict_label, accuracy, prob_values] = svmpredict(ones(size(testData,1),1), testing_instance_matrix, model, '-b 1');