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  Information Bottleneck for Non Co-Occurrence Data

Seldin, Y., Slonim, N., & Tishby, N. (2007). Information Bottleneck for Non Co-Occurrence Data. In Advances in Neural Information Processing Systems 19, 2007 (NIPS 2006), 1241-1248.

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Seldin, Y1, Autor           
Slonim, N, Autor
Tishby, N, Autor
Schölkopf, Herausgeber
B., Herausgeber
Platt, J., Herausgeber
Hofmann, T., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We present a general model-independent approach to the analysis of data in cases when these data do not appear in the form of co-occurrence of two variables X, Y, but rather as a sample of values of an unknown (stochastic) function Z(X,Y). For example, in gene expression data, the expression level Z is a function of gene X and condition Y; or in movie ratings data the rating Z is a function of viewer X and movie Y . The approach represents a consistent extension of the Information Bottleneck method that has previously relied on the availability of co-occurrence statistics. By altering the relevance variable we eliminate the need in the sample of joint distribution of all input variables. This new formulation also enables simple MDL-like model complexity control and prediction of missing values of Z. The approach is analyzed and shown to be on a par with the best known clustering algorithms for a wide range of domains. For the prediction of missing values (collaborative filtering) it improves the currently best known results.

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 Datum: 2007-09
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISBN: 0-262-19568-2
URI: http://nips.cc/Conferences/2006/
BibTex Citekey: 6576
 Art des Abschluß: -

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Titel: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Veranstaltungsort: Vancouver, BC, Canada
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Titel: In Advances in Neural Information Processing Systems 19, 2007 (NIPS 2006)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Cambridge, MA, USA : MIT Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1241 - 1248 Identifikator: -