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I have a dataset with two overlapping classes, seven points in each class, points are in two-dimensional space. In R, and I'm running svm from the e1071 package to build a separating hyperplane for these classes. I'm using the following command:

svm(x, y, scale = FALSE, type = 'C-classification', kernel = 'linear', cost = 50000)

where x contains my data points and y contains their labels. The command returns an svm-object, which I use to calculate parameters $w$ (normal vector) and $b$ (intercept) of the separating hyperplane.

Figure (a) below shows my points and the hyperplane returned by the svm command (let's call this hyperplane the optimal one). The blue point with symbol O shows the space origin, dotted lines show the margin, circled are points which have non-zero $\xi$ (slack variables).

Figure (b) shows another hyperplane, which is a parallel translation of the optimal one by 5 (b_new = b_optimal - 5). It is not difficult to see that for this hyperplane the objective function $$ 0.5||w||^2 + cost \sum \xi_i $$ (which is minimized by C-classification svm) will have lower value than for the optimal hyperplane shown in figure (a). So does it look like there is a problem with this svm function? Or did I make a mistake somewhere?

enter image description here

Below is the R code I used in this experiment.

library(e1071)

get_obj_func_info <- function(w, b, c_par, x, y) {
    xi <- rep(0, nrow(x))

    for (i in 1:nrow(x)) {
        xi[i] <- 1 - as.numeric(as.character(y[i]))*(sum(w*x[i,]) + b)
        if (xi[i] < 0) xi[i] <- 0
    }

    return(list(obj_func_value = 0.5*sqrt(sum(w * w)) + c_par*sum(xi), 
                    sum_xi = sum(xi), xi = xi))
}

x <- structure(c(41.8226593092589, 56.1773406907411, 63.3546813814822, 
66.4912298720281, 72.1002963174962, 77.649309469458, 29.0963054665561, 
38.6260575252066, 44.2351239706747, 53.7648760293253, 31.5087701279719, 
24.3314294372308, 21.9189647758150, 68.9036945334439, 26.2543850639859, 
43.7456149360141, 52.4912298720281, 20.6453186185178, 45.313889181287, 
29.7830021158501, 33.0396571934088, 17.9008386892901, 42.5694092520593, 
27.4305907479407, 49.3546813814822, 40.6090664454681, 24.2940422573947, 
36.9603428065912), .Dim = c(14L, 2L))

y <- structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L), .Label = c("-1", "1"), class = "factor")

a <- svm(x, y, scale = FALSE, type = 'C-classification', kernel = 'linear', cost = 50000)

w <- t(a$coefs) %*% a$SV;
b <- -a$rho;

obj_func_str1 <- get_obj_func_info(w, b, 50000, x, y)
obj_func_str2 <- get_obj_func_info(w, b - 5, 50000, x, y)
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  • $\begingroup$ Did you tune the cost parameter ? $\endgroup$ – Etienne Racine Aug 29 '12 at 12:40
  • $\begingroup$ Note that the BUGS tag refers to Bayesian inference Using Gibbs Sampling, not software problems. I have removed the tag. $\endgroup$ – Sycorax Sep 29 '15 at 20:46
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In the libsvm FAQ is mentioned that the labels used "inside" the algorithm can be different from yours. This will sometimes reverse the sign of the "coefs" of the model.

For instance, if you had labels $y=[-1,+1,+1,-1,...]$, then the first label in $y$, which is "-1", will be classified as $+1$ for running libsvm and, obviously, your "+1" will be classified as $-1$ inside the algorithm.

And recall that the coefs in the returned svm model are indeed $\alpha_n\,y_n$ and so your calculated $w$ vector will be affected due to reversion of the sign of the $y$'s.

See the question "Why the sign of predicted labels and decision values are sometimes reversed?" here.

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I've run into the same problem using LIBSVM in MATLAB. To test it, I created a very simple, 2D linearly separable data set that happened to be translated along one axis to out around -100. Training a linear svm using LIBSVM produced a hyperplane whose intercept was still right around zero (and so the error rate was 50%, naturally). Standardizing the data (subtracting the mean) helped, though the resulting svm still did not perform perfectly...perplexing. It seems as though LIBSVM only rotates the hyperplane about the axis without translating it. Perhaps you should try subtracting the mean from your data, but it seems odd that LIBSVM would behave this way. Perhaps we are missing something.

For what it's worth, the built-in MATLAB function svmtrain produced a classifier with 100% accuracy, without standardization.

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