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  Partial Least Squares Regression for Graph Mining

Saigo, H., Krämer, N., & Tsuda, K. (2008). Partial Least Squares Regression for Graph Mining. In Y. Li, B. Liu, & S. Sarawagi (Eds.), KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 578-586). New York, NY, USA: ACM Press.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C7BC-7 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0003-3D39-A
Genre: Konferenzbeitrag

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externe Referenz:
https://dl.acm.org/citation.cfm?doid=1401890.1401961 (Verlagsversion)
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Urheber

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 Urheber:
Saigo, H1, 2, Autor           
Krämer, N, Autor
Tsuda, K1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2biological cy, ou_persistent22              

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Schlagwörter: -
 Zusammenfassung: Attributed graphs are increasingly more common in many application
domains such as chemistry, biology and text processing.
A central issue in graph mining is how to collect informative subgraph
patterns for a given learning task.
We propose an iterative mining method based on partial least squares regression (PLS). To apply PLS to graph data, a sparse version of PLS is developed first and then it is combined with a weighted pattern mining algorithm.
The mining algorithm is iteratively called with different weight
vectors, creating one latent component per one mining call.
Our method, graph PLS, is efficient and easy to implement, because the
weight vector is updated with elementary matrix calculations.
In experiments, our graph PLS algorithm showed
competitive prediction accuracies in many chemical datasets and its
efficiency was significantly superior to graph boosting (gboost) and the
naive method based on frequent graph mining.

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Sprache(n):
 Datum: 2008-08
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1145/1401890.1401961
BibTex Citekey: 5204
 Art des Abschluß: -

Veranstaltung

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Titel: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Veranstaltungsort: Las Vegas, NV, USA
Start-/Enddatum: 2008-08-24 - 2008-08-27

Entscheidung

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Projektinformation

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Quelle 1

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Titel: KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Genre der Quelle: Konferenzband
 Urheber:
Li, Y, Herausgeber
Liu, B, Herausgeber
Sarawagi, S, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: New York, NY, USA : ACM Press
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 578 - 586 Identifikator: ISBN: 978-1-60558-193-4