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Spectral classification with principal component analysis and artificial neural networks

Storrie-Lombardi, M. C. and Irwin, M. J. and von Hippel, T. and Storrie-Lombardi, L. J. (1994) Spectral classification with principal component analysis and artificial neural networks. Vistas in Astronomy, 38 (3). 331-IN8. ISSN 0083-6656. https://resolver.caltech.edu/CaltechAUTHORS:20170329-111139163

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Abstract

Derived from non-linear signal processing strategies common to biological systems, neural network algorithms generalise classical data analysis techniques, e.g. Fourier analysis, Wiener filtering, and vector clustering algorithms. Conversely, multifactor analysis tools such as principal component analysis can function in a manner analogous to that of an unsupervised neural network. We have explored the use of principal component analysis for data pre-processing prior to classification of stellar spectra with a non-linear neural network. The strategy significantly enhances classification replicability, network stability, and convergence.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/0083-6656(94)90044-2DOIArticle
http://www.sciencedirect.com/science/article/pii/0083665694900442?via%3DihubPublisherArticle
ORCID:
AuthorORCID
Storrie-Lombardi, L. J.0000-0002-5987-5210
Additional Information:© 1995 Elsevier B.V.
Subject Keywords:principal component analysis; neural networks; spectra; classification; stars
Issue or Number:3
Record Number:CaltechAUTHORS:20170329-111139163
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170329-111139163
Official Citation:M.C. Storrie-Lombardi, M.J. Irwin, T. von Hippel, L.J. Storrie-Lombardi, Spectral classification with principal component analysis and artificial neural networks, Vistas in Astronomy, Volume 38, 1994, Pages 331-IN8, ISSN 0083-6656, http://dx.doi.org/10.1016/0083-6656(94)90044-2. (http://www.sciencedirect.com/science/article/pii/0083665694900442)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:75510
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:29 Mar 2017 19:06
Last Modified:03 Oct 2019 16:51

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