Abstract
Hubble’s morphological classification of galaxies
has found broad acceptance in astronomy since decades.
Numerous extensions have been proposed in the past,
mostly based on galaxy prototypes. In this work, we
automatically learn morphological maps of galaxies with
unsupervised machine learning methods that preserve
neighborhood relations and data space distances. For
this sake, we focus on a stochastic variant of
unsupervised nearest neighbors (UNN) for arranging
galaxy prototypes on a two-dimensional map. \UNN\
regression is the unsupervised counterpart of nearest
neighbor regression for dimensionally reduction. In the
experimental part of this article, we visualize the
embeddings and compare the learning results achieved by
various \UNN\ parameterizations and related
dimensionality reduction methods.
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