Aim of this work is to explore the effectiveness of theoretical infor7
mation approaches for the reduction of data complexity in multi-model
ensemble systems. We first exploit a weak form of independence, i.e. uncorrelation, as a
mechanism for detecting linear relationships. Then, stronger and more
general forms of independence measure, such as Mutual Information, are
used to investigate dependence structures for models data selection.
A distance matrix, measuring the inter-dependence between data, is
derived for the investigated measures, with the scope of clustering cor related/dependent models together. Redundant information is discarded by selecting a few representative models from each cluster.
RICCIO Angelo;
CIARAMELLA Angelo;
GIUNTA Giulio;
GALMARINI Stefano;
POTEMPSKI Slawomir;
SOLAZZO Efisio;
2012-12-05
AMER GEOPHYSICAL UNION
JRC65675
0148-0227,
http://www.agu.org/pubs/crossref/2012/2011JD016503.shtml,
https://publications.jrc.ec.europa.eu/repository/handle/JRC65675,
10.1029/2011JD016503,
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