Astronomers teach a machine to see

Jim Geach writes: A team of astronomers and computer scientists have developed a novel unsupervised machine learning algorithm — a combination of Growing Neural Gas and Hierarchical Clustering — to automatically analyse astronomical images. In effect, the algorithm performs the same task as a human 'eyeballing' an image, automatically identifying and labelling the points of interest. The team is aiming to deploy the algorithm on the next generation of astronomical surveys such as LSST and Euclid, but note that the algorithm could also find application in other fields, such as medical imaging and early disease diagnosis. Team member Dr Jim Geach said

Our aim is to deploy this tool on the next generation of giant imaging surveys where no human, or even group of humans, could closely inspect every piece of data. But this algorithm has a huge number of applications far beyond astronomy, and investigating these applications will be our next step

The results are being presented at the UK National Astronomy Meeting in Wales, and the details of the algorithm are described in this paper http://arxiv.org/abs/1507.0158...