VLFeat - Tutorials > Support Vector Machines (SVMs)

# VLFeat.org

Tutorials> Support Vector Machines (SVMs)

VLFeat includes fast SVM solvers, SGC [1] and (S)DCA [2], both implemented in `` vl_svmtrain. The function also implements features, like Homogeneous kernel map expansion and SVM online statistics. (S)DCA can also be used with different loss functions.

# Support vector machine

A simple example on how to use `` vl_svmtrain is presented below. Let's first load and plot the training data:

    % Load training data X and their labels y
    vl_setup demo % to load the demo data
    load('vl_demo_svm_data.mat');

    Xp = X(:,y==1);
    Xn = X(:,y==-1);

    figure
    plot(Xn(1,:),Xn(2,:),'*r')
    hold on
    plot(Xp(1,:),Xp(2,:),'*b')
    axis equal ;

Now we have a plot of the tutorial training data:

Training Data.

Now we will set the learning parameters:

    lambda = 0.01 ; % Regularization parameter
    maxIter = 1000 ; % Maximum number of iterations

Learning a linear classifier can be easily done with the following 1 line of code:

    [w b info] = vl_svmtrain(X, y, lambda, 'MaxNumIterations', maxIter)

Now we can plot the output model over the training data.

    % Visualisation
    eq = [num2str(w(1)) '*x+' num2str(w(2)) '*y+' num2str(b)];
    line = ezplot(eq, [-0.9 0.9 -0.9 0.9]);
    set(line, 'Color', [0 0.8 0],'linewidth', 2);

The result is plotted in the following figure.

Learned model.

The output ``info is a struct containing some statistic on the learned SVM:

    info =

                solver: 'sdca'
                lambda: 0.0100
        biasMultiplier: 1
                  bias: 0.0657
             objective: 0.2105
           regularizer: 0.0726
                  loss: 0.1379
         dualObjective: 0.2016
              dualLoss: 0.2742
            dualityGap: 0.0088
             iteration: 525
                 epoch: 3
           elapsedTime: 0.0300

It is also possible to use under some assumptions [3] a homogeneous kernel map expanded online inside the solver. This can be done with the following commands:

    % create a structure with kernel map parameters
    hom.kernel = 'KChi2';
    hom.order = 2;
    % create the dataset structure
    dataset = vl_svmdataset(X, 'homkermap', hom);
    % learn the SVM with online kernel map expansion using the dataset structure
    [w b info] = vl_svmtrain(dataset, y, lambda, 'MaxNumIterations', maxIter)

The above code creates a training set without applying any homogeneous kernel map to the data. When the solver is called it will expand each data point with a Chi Squared kernel of period 2.

# Diagnostics

VLFeat allows to get statistics during the training process. It is sufficient to pass a function handle to the solver. The function will be then called every ``DiagnosticFrequency time.

(S)DCA diagnostics also provides the duality gap value (the difference between primal and dual energy), which is the upper bound of the primal task sub-optimality.

    % Diagnostic function
    function diagnostics(svm)
      energy = [energy [svm.objective ; svm.dualObjective ; svm.dualityGap ] ] ;
    end

    % Training the SVM
    energy = [] ;
    [w b info] = vl_svmtrain(X, y, lambda,...
                               'MaxNumIterations',maxIter,...
                               'DiagnosticFunction',@diagnostics,...
                               'DiagnosticFrequency',1)

The objective values for the past iterations are kept in the matrix ``energy. Now we can plot the objective values from the learning process.

    figure
    hold on
    plot(energy(1,:),'--b') ;
    plot(energy(2,:),'-.g') ;
    plot(energy(3,:),'r') ;
    legend('Primal objective','Dual objective','Duality gap')
    xlabel('Diagnostics iteration')
    ylabel('Energy')

SVM objective values plot.

# References

- [1] Y. Singer and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In Proc. ICML, 2007.
- [2] S. Shalev-Schwartz and T. Zhang. Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization. 2013.
- [3] A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In PAMI, 2011.