#AAAC, day 2, classification by discriminability

The morning started with the second day of the #AAAC meeting. Steve Kahn (Stanford) talked to us about the relationships among the superficially similar projects LSST, Euclid, and WFIRST. The argument is that they are highly complementary. I didn't really disagree, but it is not obvious that we as a community would be willing to spend a lot of money on WFIRST if we knew that LSST and Euclid are definitely going forward. I asked pointed questions and hope to follow up. Since WFIRST can do so many things, maybe it should slightly re-prioritize given the context?

In the afternoon, I talked with Amit Singer (Princeton), who was pretty adamant that the stuff I am doing on single-photon imaging is stupid and a waste of time! Late in the day, based on a comment by Greengard, Jeremy Magland and I formulated an awesome new clustering (or unsupervised classification) algorithm: Define discriminability (of j from k) to be the empirical probability that a point from distribution j be closer to a neighbor in distribution j than to a point in distribution k. Now set the boundary (which could be an arbitrarily shaped surface) to maximize discriminability. Magland ended up getting pessimistic when we realized that it would be slow. But it is worth exploring.

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