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| Paper: |
Photometric Redshift Estimation of Quasars: Local versus Global Regression |
| Volume: |
461, Astronomical Data Analysis Software and Systems XXI |
| Page: |
537 |
| Authors: |
Gieseke, F.; Polsterer, K. L.; Zinn, P.-C. |
| Abstract: |
The task of estimating an object's redshift based on photometric data is one of
the most important ones in astronomy. This is especially the case for
quasars. Common approaches for this regression task are based on nearest
neighbor search, template fitting schemes, or combinations of, e.g., clustering
and regression techniques. As we show in this work, simple frameworks like
k-nearest neighbor regression work extremely well if one considers the overall
feature space (containing patterns of all objects with low, middle, and high
redshifts). However, such methods naturally fail as soon as only very few or
even no training patterns are given in the appropriate region of the feature
space. In the literature, a wide range of other regression techniques can be
found. Among the most popular ones are regularized regression schemes like ridge
regression or support vector regression. In this work, we show that an
out-of-the-box application of this type of schemes for the whole feature space
is difficult due to the involved computational requirements and the specific
properties of the data at hand. However, in contrast to nearest neighbor search
schemes, such methods can be employed to extrapolate, i.e., they can be used to
predict redshifts for patterns in new, unseen regions of the feature space. |
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