Heh! indeed, its all geometry :) Happy to hear back from you. I am actually gladdened that your anomaly detection work is getting some interest on HN lately. H

Heh! indeed, its all geometry :) Happy to hear back from you. I am actually gladdened that your anomaly detection work is getting some interest on HN lately. Hope something comes out of it. I am now slowly coming to the conclusion that pushing better methods on an existing stack would be really hard. Too much friction, too much politics. Perhaps the way is to create your own better cloud of servers, but that's really big league stuff. Not sure I have the stomach for that. Curious if you have given any thought to the choice of the metric space where you define your statistics. That might play an important role from what I have seen. There might be an interesting manifold story there. Big spoilsports are non-stationarities and even bigger are those fat tails. If only everything had a moment generating function. I see that you have been pointed to Abu-Mostafa he is definitely a good source. Not that Andrew Ng is unaware of the stuff, far from it, he is fighting a different battle: to make parts of ML a commodity. If you have time then you can browse the proceedings of COLT (conference on learning theory) and ICML. > or maybe I'll just derive it myself again. You almost always have to derive it yourself anyway even after you have seen the derivation by somebody else.