Abstract
Progress is reported on a project which aims at mapping the extragalactic sky in order to derive the large scale distribution of luminous matter. Our approach consists in selecting from the IRAS Point Source Catalog a set of galaxies which is as clean and as complete as possible. The decision and discrimination problems involved lend themselves to a treatment using methods from multivariate statistics, in particular statistical pattern recognition. Two different approaches - one based on supervised Bayesian classification, the other on unsupervised data-driven classification - are presented and some preliminary results are reported.
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Adorf, H.M., Meurs, E.J.A. (1988). Supervised and unsupervised classification — The case of IRAS point sources. In: Seitter, W.C., Duerbeck, H.W., Tacke, M. (eds) Large-Scale Structures in the Universe Observational and Analytical Methods. Lecture Notes in Physics, vol 310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-50135-5_86
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DOI: https://doi.org/10.1007/3-540-50135-5_86
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