Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Low-Dimensional Structure in Astronomical Spectra   Dimension-reduction techniques can greatly improve statistical inference in
astronomy. A standard approach is to use Principal Components Analysis (PCA).
In this work we apply a recently-developed technique, diffusion maps, to
astronomical spectra for data parameterization and dimensionality reduction,
and develop a robust, eigenmode-based framework for regression. We show how our
framework provides a computationally efficient means by which to predict
redshifts of galaxies, and thus could inform more expensive redshift estimators
such as template cross-correlation. It also provides a natural means by which
to identify outliers (e.g., misclassified spectra, spectra with anomalous
features). We analyze 3835 SDSS spectra and show how our framework yields a
more than 95% reduction in dimensionality. Finally, we show that the prediction
error of the diffusion map-based regression approach is markedly smaller than
that of a similar approach based on PCA, clearly demonstrating the superiority
of diffusion maps over PCA for this regression task.

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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th