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- Title
MODEL-BASED CLUSTERING OF HIGH-DIMENSIONAL DATA IN ASTROPHYSICS.
- Authors
Bouveyron, C.
- Abstract
The nature of data in Astrophysics has changed, as in other scientific fields, in the past decades due to the increase of the measurement capabilities. As a consequence, data are nowadays frequently of high dimensionality and available in mass or stream. Model-based techniques for clustering are popular tools which are renowned for their probabilistic foundations and their flexibility. However, classical model- based techniques show a disappointing behavior in high-dimensional spaces which is mainly due to their dramatical over-parametrization. The recent developments in model-based classification overcome these drawbacks and allow to efficiently classify high-dimensional data, even in the "small n / large p" situation. This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based on variable selection. The use of these model-based methods is also illustrated on real-world classification problems in Astrophysics using R packages.
- Subjects
ASTROPHYSICS; MATHEMATICAL variables; PARSIMONIOUS models; PROBABILISTIC number theory; SUBSPACES (Mathematics)
- Publication
EAS Publications Series, 2016, Vol 77, p91
- ISSN
1633-4760
- Publication type
Article
- DOI
10.1051/eas/1677006