What's the reference of one-class svm with negative samples?
BTW the optimization algorithm in libsvm for one-class
svm does assume y=1.. So you cannot just change y to have 1 and -1.
Lukáš Ondráček writes:

• learning now uses both positive and negaive examples
• array y should contain +1s for positive examples and -1s for
  negative
• based on the "One-class classification" thesis written by door
  David Martinus Johannes TAX

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#74

Commit Summary

• Support for negative examples in one-class added

File Changes

• M README (7)
• M svm.cpp (21)

Patch Links:

• https://github.com/cjlin1/libsvm/pull/74.patch
• https://github.com/cjlin1/libsvm/pull/74.diff

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What's the reference of one-class svm with negative samples?

It is described in Section 2.2 of the thesis:
http://homepage.tudelft.nl/n9d04/thesis.pdf

Your modification doesn't solve their opt problem. See solve_one_class() in svm.cpp:

for(i=0;i<n;i++)
    alpha[i] = 1;
if(n<prob->l)
    alpha[n] = param->nu * prob->l - n;

The init sol is under the assumption of e^Talpha = a positive constant.

Theirs is an extension of SVDD so you should check the SVDD code in libsvmtools

Lukáš Ondráček writes:

What's the reference of one-class svm with negative samples?

It is described in Section 2.2 of the thesis:
http://homepage.tudelft.nl/n9d04/thesis.pdf

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I am sorry for my poor knowledge of SVMs,
can you please explain in more detail what's wrong?

Your modification doesn't solve their opt problem. See solve_one_class() in svm.cpp:

for(i=0;i<n;i++)
alpha[i] = 1;
if(nl)
alpha[n] = param->nu * prob->l - n;

The init sol is under the assumption of e^Talpha = a positive constant.

What does the e vector mean?
I haven't found it anywhere.

Why should y be non-negative here,
while it is not needed in other occurrences (like in SVR)?

Maybe you have meant y instead of e;
in such a case I would be able to answer the previous question,
but from what the assumption follows?

Theirs is an extension of SVDD so you should check the SVDD code in libsvmtools

Which part of code do you mean?

In the thesis,
description of the model is extended
by the vector y of +/- ones,
but then it is shown,
that the signs can be incorporated in the vector alpha,
so that the prediction works the same way as without negative samples.
I change the signs of the vector just after calling the Solve method.