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- W2567158557 abstract "Abstract This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or ‘photo-z’ problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts." @default.
- W2567158557 created "2017-01-06" @default.
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- W2567158557 date "2016-10-01" @default.
- W2567158557 modified "2023-09-24" @default.
- W2567158557 title "Hierarchical Matching and Regression with Application to Photometric Redshift Estimation" @default.
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- W2567158557 doi "https://doi.org/10.1017/s1743921317001569" @default.
- W2567158557 hasPublicationYear "2016" @default.
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