Finding Hidden Events in Astrophysical Data using PCA and Mixture of Gaussians Clustering View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-05

AUTHORS

Maria Funaro, Maria Marinaro, Alfredo Petrosino, Silvia Scarpetta

ABSTRACT

The Principal Component Analysis (PCA) is applied to a set of astronomic data to obtain a separation between variations of luminosity and noisy fluctuations. A clustering with the Mixture of Gaussians method, performed in the principal subspace, allows us to classify the data according to the features of interest. Our results are compared with those obtained by the AGAPE (Andromeda Galaxy and Amplified Pixels Experiment) collaboration. More... »

PAGES

15-22

Journal

TITLE

Pattern Analysis and Applications

ISSUE

1

VOLUME

5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s100440200002

DOI

http://dx.doi.org/10.1007/s100440200002

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1010579560


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