A particle swarm embedding algorithm for nonlinear dimensionality reduction
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Learning morphological maps of galaxies with unsupervised regression
Expert Systems with Applications: An International Journal
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We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained via approaches working only on photometric data, our experiments indicate that simple but problem-specific features extracted from spectroscopic data can significantly improve the classification performance. Since our approach works orthogonal to existing classification schemes used for building the spectroscopic catalogs, our classification results are well suited for a mutual assessment of the approaches' accuracies.