Peekaboo

As I have been working on some clustering algorithms recently, I invested some time last weekend to refactor some code inside sklearn to generate some toy data sets to visualize the results of clustering algorithms. That looks something like this: While the first two are nice to show off that your algorithm can handle non-convex clusters, these data sets obviously look nothing like the data you'll see in practice. So I wanted to have some a bit more general data set generator. What I ended up doing is a nonparametric mixture of Gaussians. While Gaussians are a bit boring, combining them with a non-parametric prior makes them somewhat more general. As I didn't found some very easy to use package to do that (though David pointed out p ymc ) I went ahead and wrote the generative model down myself. It's a mixture of Gaussians with a Chinese restaurant process as prior for the mixture components and Wishard-Gaussian priors for mean and variance. You can find the ...

Ok this is probably my last post about NIPS 2010. First of all, I became a big fan of Zoubin Ghahramani . He is a great speaker and quite funny. There are quite some video lecture by him that are linked on his personal page: here and here . They are mostly about graphical models and nonparametric methods. He had an invited talk at the transfer learning workshop about cascading indian buffet process where he illustrated the idea behind this method: "Every dish is a customer in another restaurant. Somebody pointed out that this is kind of canabilistic. We didn't realize that the IBP analogy goes really deep.... dark ... and wrong." This work is about learning the structure of directed graphical models using IBP priors on the graph structure ( pdf ). When asked about three way interaction, which this model does not feature - in contrast to many deep graphical models studied at the moment - he argued that latent variables induce covariances by marginalization on the lay...