Machine learning and image processing in astronomy with sparse data sets

Automated classification systems have allowed for the rapid development of digital large sky surveys. Such systems increase the independence of human intervention in the analysis stage of star and galaxy classification. Artificial neural networks, hierarchical classifiers and ensembles of classifiers have been used as the methods of classification in these systems. This paper investigates the development of an automated classification system for galaxies in astronomical images based on the method of sparse representation. The dependency of classification based on image enhancement by the alpha-rooting, heap-, and paired-transforms is secondarily investigated.