spectral age indicators, structured learning

Before group meeting, Ness and I discussed the scope of a paper that separates red-clump stars from ordinary red-giant stars, using the data-driven spectral model we call The Cannon. We discussed also the possibility that this could turn into a set of spectral age indicators: If we can separate the red clump from the red-giant branch, maybe we can split the red-giant branch into the three nearly overlapping branches on which stars rise and fall as they age.

Andreas Mueller (NYU), one of the principal developers of scikit-learn joined my group meeting today. He told us about structured learning, in which you augment learning based on features with other kind of structural information, usually represented as graph edges or even graph edges with features themselves. Key example: If you want to know what pixels in an image are sky pixels, you are interested in their color, but also their proximity to neighboring pixels that are also labeled as sky pixels (or not). He is building, documenting, and maintaining an open-source package called pyStruct.

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