Some thinking on Parzen window and Semi-supervised learning

Here is an idea originates from the following refereces:
Pattern Classification, by O. Duda: It presents me a basic form of Parzen window. And I think it is quite natural to use other distributons instead of a uniform distribution in a super cube. So I wondered where I can find more information on this topic. PageRank then suggested severl useful links and mnauce gave several more and comments.

• Emaneul Parzen, On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3):1065-1076, 1962.<available from JSTOR>
• B. Silverman, Density estimation for statistics and data analysis, Chapman and Hall, London, 1986<books.google>
• Asymptotic Statistics, by Vann Der vaart<in library>
• A framework for probability density estimation, by John Shawe-Taylor<PASCAL>

Learning with Kernels, by Bernhard Schölkopf and Alex Smola: There is a simple mean classifier in it which indicates that kernel framework will convert many algorithms to superior ones. And then it tells us this classifier can be taken as a difference of Parzen windows. And then I think about SVM. The solution of a SVM tells us which samples are necessary to establish a discriminant classifier and what's their weight.

Manifold Parzen Window, by Vincent, Bengio(in NIPS 2003): They suggested a method to incorporate manifold learning ideas with density estimation. The final version is not far from what I have thought about the problem, local PCA just as MFA does. But I suddenly realize that the neighborhood sellection method used by Zhenyue Zhang is also feasible here. However they indeed extended this version in their later work.

Non-local Manifold Parzen Window, by by Vincent, Bengio(in NIPS 2005). However, then they still stayed at manifold learning. They haven't progressed as fast as I imagined. Though I am still confused about the value of this work now(am silly), I guess there can be something if we try to put the problem in the field of semi-supervised learning paradigm.