%0 Journal Article %1 kramer-learning-morphological-galaxies-2013 %A Kramer, Oliver %A Gieseke, Fabian %A Polsterer, Kai Lars %D 2013 %J Expert Systems with Applications %K galaxies morphological unsupervised %N 8 %P 2841--2844 %R 10.1016/j.eswa.2012.12.002 %T Learning morphological maps of galaxies with unsupervised regression %U http://www.sciencedirect.com/science/article/pii/S0957417412012432 %V 40 %X 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.

@article{kramer-learning-morphological-galaxies-2013, 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.}, added-at = {2016-07-12T19:24:18.000+0200}, author = {Kramer, Oliver and Gieseke, Fabian and Polsterer, Kai Lars}, biburl = {https://www.bibsonomy.org/bibtex/2879f5841da50686da8840848d2cdfc00/mhwombat}, doi = {10.1016/j.eswa.2012.12.002}, interhash = {ffe841bc0fea3479a5ab3ccb80e2ae2e}, intrahash = {879f5841da50686da8840848d2cdfc00}, issn = {0957-4174}, journal = {Expert Systems with Applications}, keywords = {galaxies morphological unsupervised}, number = 8, pages = {2841--2844}, timestamp = {2016-07-12T19:25:30.000+0200}, title = {Learning morphological maps of galaxies with unsupervised regression}, url = {http://www.sciencedirect.com/science/article/pii/S0957417412012432}, volume = 40, year = 2013 }