I'm interested in the possiblity of using support vector machines for density estimation. The main source I found for this is a paper by Mukherjee & Vapnik 'Support vector method for multivariate density estimation' (Link) which claims that this approach performs better than for example the Parzen-Window approach. The scikit-learn documentation mentions the possibility of using SVMs for this purpose, however there's no clear description of it to be found.
The paper by Mukherjee & Vapnik presents a different optimization problem that is to be solved using the support vector approach for this, while the scikit-learn documentation implies the use of one-class SVMs for this.
Do these two approaches amount to different results? Would an estimation as presented in the Mukherjee & Vapnik paper amount to the same as a one-class-SVM density estimation as shown in the scikit-learn example, if done for both classes in a binary problem?
I have failed to find a reference implementation of the approach by Mukherjee & Vapnik. Is anyone aware of an implementation that I could have a look at?
Can anyone provide further material on this matter?