Interesting comparison, but only using generated mixtures of gaussians for training data severely limits any conclusions that can be drawn from this. Naturally the method with the same assumptions as the generating process had the best performance.
It is important to note that both the performance of the machine learning algorithm (in terms of the error metric) and its runtime are very dependent on the source data in most cases.
I was impressed that random forests did so well for the irrelevant feature detection, given that they know nothing about gaussians. Though IIRC, you've used them to win Kaggle competitions, so maybe you already know their power.
The author of the blog post has promised more extensive tests with other datasets in future posts. Also there is an ongoing GSoC project for setting up a systematic benchmark infrastructure + performance optimizations for the scikit-learn project. So let's wait and see. We will soon have more meat for finer analysis.
It is important to note that both the performance of the machine learning algorithm (in terms of the error metric) and its runtime are very dependent on the source data in most cases.