I simulate a simple linear setup:

n = 1000
X = runif(n)
Y = runif(n)

ind = X + 2*Y < 1
ind[ind == TRUE] = runif(sum(ind)) < 1
plot(X,Y,col = ind + 1)

Which gives

linear split

The svm() functcion from e1071 performs very well but it gives me a lot of vectors.

svm(formula = ind ~ X + Y, type = "C-classification", kernel = "linear")

   SVM-Type:  C-classification 
 SVM-Kernel:  linear 
       cost:  1 

Number of Support Vectors:  99

Can you please tell me what and how should I tune to get one vector (or just a few)?

  • 2
    $\begingroup$ Increase the value of the cost parameter to have less support vectors. If the kernel you use is linear, gamma is not used. $\endgroup$ Nov 24, 2014 at 12:49
  • $\begingroup$ Thanks a lot, that works! gamma was there from my another tests, I will update the code. $\endgroup$ Nov 24, 2014 at 12:53
  • $\begingroup$ It would be fun to use cross validation to see what is the optimal value for this cost parameter ! $\endgroup$ Nov 24, 2014 at 12:57
  • $\begingroup$ For sure. In fact in the e1071 package there is a tune.svm procedure, which takes a given set of parameters and does CV automatically, but this is a bit different story. Thanks for helping with this one :) $\endgroup$ Nov 24, 2014 at 13:03
  • 1
    $\begingroup$ In my experience, tuning the regularisation parameter using CV generally results in models with lots of support vectors. I would view the sparsity of SVM as a convenient by-product, but nothing more. If you really want a sparse model, try something like L1 regularised logistic regression. $\endgroup$ Nov 24, 2014 at 14:52

1 Answer 1


Any linear SVM can be summarized as a single vector in input space:

$$f(\mathbf{z}) = \sum_{i \in SV} y_i \alpha_i \mathbf{x}_i^T\mathbf{z} +b,$$

can be rewritten as:

$$f(\mathbf{z}) = \mathbf{w}^T\mathbf{z} + b,$$


$$w[k] = \sum_{i\in SV} y_i \alpha_i x_i[k], \quad k=1..d$$

The amount of support vectors that actually form the model is not that relevant for a linear SVM, except for prediction speed (the above comment applies).

The problem here is that e1071 apparently uses LIBSVM instead of LIBLINEAR for linear SVM's. LIBSVM doesn't turn a linear model into a single vector in input space.


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