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  Nonlinear directed acyclic structure learning with weakly additive noise models

Tillman, R., Gretton, A., & Spirtes, P. (2010). Nonlinear directed acyclic structure learning with weakly additive noise models. Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009, 1847-1855.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C0CE-C Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C0CF-A
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 Urheber:
Tillman, RE, Autor
Gretton, A1, Autor           
Spirtes, P, Autor
Bengio, Herausgeber
Y., Herausgeber
Schuurmans, D., Herausgeber
Lafferty, J., Herausgeber
Williams, C., Herausgeber
Culotta, A., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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Schlagwörter: -
 Zusammenfassung: The recently proposed emphadditive noise model has advantages over previous structure learning algorithms, when attempting to recover some true data generating mechanism, since it (i) does not assume linearity or Gaussianity and (ii) can recover a unique DAG rather than an equivalence class. However, its original extension to the multivariate case required enumerating all possible DAGs, and for some special distributions, e.g. linear Gaussian, the model is invertible and thus cannot be used for structure learning. We present a new approach which combines a PC style search using recent advances in kernel measures of conditional dependence with local searches for additive noise models in substructures of the equivalence class. This results in a more computationally efficient approach that is useful for arbitrary distributions even when additive noise models are invertible. Experiments with synthetic and real data show that this method is more accurate than previous methods when data are nonlinear and/or non-Gaussian.

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 Datum: 2010-04
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISBN: 978-1-615-67911-9
URI: http://nips.cc/Conferences/2009/
BibTex Citekey: 6133
 Art des Abschluß: -

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Titel: 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009)
Veranstaltungsort: Vancouver, BC, Canada
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Titel: Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1847 - 1855 Identifikator: -