Kristoffer Stensbo-Smidt

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Data in astronomy is rapidly growing with upcoming surveys producing 30 TB of images per night. Highly informative spectra are too expensive to measure for each detected object, hence ways of reliably estimating physical properties from images alone are paramount. The objective of this work is to test whether a “big data ready” k-nearest(More)
A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to(More)
Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night, and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular,(More)
The perceptron algorithm is an algorithm for supervised linear classification. Restricting ourselves to considering only binary classification, the most basic linear classifier is a hyperplane separating the two classes of our dataset. More formally, assume that we have a normal vector w ∈ RD defining a hyperplane. Binary classification is typically(More)
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