linear classifier for projections, Herschel

In the morning, Joakim Andén (Princeton) spoke at the SCDA about classifying noisy cryo-EM projected molecules into different categories coming from different (but similar) conformations of the same basic structure. These are subtle differences, and each data point (which is a tiny, noisy image) is only a randomly oriented projection of the molecule in question. He develops the best linear discriminant by finding the eigenvalues of the data matrix in the three-dimensional space, which is clever because he only has the data in (many) two-dimensional projections. His method works well and is fast. This is very relevant to the galaxy deprojection project I have going with Baron. The only significant issue with the method is that it assumes that the angles for the projections are known. They aren't really; it is interesting to think about the generalization to the case of unknown projection angles.

In the afternoon, Kapala (Cape Town), Lang, and I assigned short-term tasks for our Herschel dust-mapping project: Kapala will get the data in order and comment and understand the (legacy) code. Dustin will get the code working and delivering results, and I will write the abstract and outline the paper. We worked a bit on the title late in the day.

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