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  Measuring Statistical Dependence via the Mutual Information Dimension

Sugiyama, M., & Borgwardt, K. (2013). Measuring Statistical Dependence via the Mutual Information Dimension. In F. Rossi (Ed.), Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013 ) (pp. 1692-1698). Menlo Park, CA, USA: AAAI Press.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000A-B39A-E Versions-Permalink: https://hdl.handle.net/21.11116/0000-000A-B39C-C
Genre: Konferenzbeitrag

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externe Referenz:
https://www.ijcai.org/Proceedings/13/Papers/251.pdf (Verlagsversion)
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 Urheber:
Sugiyama, M1, Autor           
Borgwardt, KM1, Autor           
Affiliations:
1Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3375790              

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Schlagwörter: -
 Zusammenfassung: We propose to measure statistical dependence between two random variables by the mutual information dimension (MID), and present a scalable parameter-free estimation method for this task. Supported by sound dimension theory, our method gives an effective solution to the problem of detecting interesting relationships of variables in massive data, which is nowadays a heavily studied topic in many scientific disciplines. Different from classical Pearson's correlation coefficient, MID is zero if and only if two random variables are statistically independent and is translation and scaling invariant. We experimentally show superior performance of MID in detecting various types of relationships in the presence of noise data. Moreover, we illustrate that MID can be effectively used for feature selection in regression.

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Sprache(n): eng - English
 Datum: 2013
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: -
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013 )
Veranstaltungsort: Beijing, China
Start-/Enddatum: 2013-08-03 - 2013-08-09

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Titel: Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 2013 )
Genre der Quelle: Konferenzband
 Urheber:
Rossi, F1, Herausgeber
Affiliations:
1 University of Padova, ou_persistent22            
Ort, Verlag, Ausgabe: Menlo Park, CA, USA : AAAI Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1692 - 1698 Identifikator: ISBN: 978-1-57735-633-2