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# Matlab multidimensional feature SVM

Marcus
1#
Marcus Published in 2018-02-13 13:33:59Z
 I have a little problem with SVM classifier in Matlab. I have a 61200x59 matrix of features in which each row represent a set of features extracted from an image (all double values). All those features are associated with a 61200x1 matrix containing 2 labels: 0 and 1 (as double variables). Now I want to train a linear classifier and I've used the following function: SVM_Model = fitcsvm(train_features, train_labels, 'KernelFunction', 'linear')  If I take a look to the details of this line I obtain this result: SVM_Model = ClassificationSVM ResponseName: 'Y' CategoricalPredictors: [] ClassNames: [0 1] ScoreTransform: 'none' NumObservations: 61200 Alpha: [40956×1 double] Bias: 0.9998 KernelParameters: [1×1 struct] BoxConstraints: [61200×1 double] ConvergenceInfo: [1×1 struct] IsSupportVector: [61200×1 logical] Solver: 'SMO'  But when I call the predict function on test set ([label, score] = predict(SVM_Model, test_features(i, :));) it predicts always the label 1 (on over than 15000 test, so it seems a little bit suspicious) and on all the class 0 object I have a classification error. Can anyone tell me what can be the problem? Is necessary to rescale the features because SVM cannot handle high-dimensional points? Or another problem is present (like misconfiguration is SVM learning)?
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