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  Learning output kernels with block coordinate descent

Dinuzzo, F., Ong, C., Gehler, P., & Pillonetto, G. (2011). Learning output kernels with block coordinate descent. In L. Getoor, & T. Scheffer (Eds.), 28th International Conference on Machine Learning (ICML 2011) (pp. 49-56). Madison, WI, USA: International Machine Learning Society.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BB22-D Versions-Permalink: https://hdl.handle.net/21.11116/0000-0002-0A99-7
Genre: Konferenzbeitrag

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http://www.icml-2011.org/ (Inhaltsverzeichnis)
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Urheber

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 Urheber:
Dinuzzo, F1, Autor           
Ong, CS, Autor           
Gehler, PV, Autor           
Pillonetto, G, Autor
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

Inhalt

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Schlagwörter: -
 Zusammenfassung: We propose a method to learn simultaneously a vector-valued function and a kernel between its components. The obtained kernel can be used both to improve learning performances and to reveal structures in the output space which may be important in their own right. Our method is based on the solution of a suitable regularization problem over a reproducing kernel Hilbert space (RKHS) of vector-valued functions. Although the regularized risk functional is non-convex, we show that it is invex, implying that all local minimizers are global minimizers. We derive a block-wise coordinate descent method that efficiently exploits the structure of the objective functional. Then, we empirically demonstrate that the proposed method can improve classification accuracy. Finally, we provide a visual interpretation of the learned kernel matrix for some well known datasets.

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Sprache(n):
 Datum: 2011-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: BibTex Citekey: DinuzzoOGP2011
 Art des Abschluß: -

Veranstaltung

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Titel: 28th International Conference on Machine Learning (ICML 2011)
Veranstaltungsort: Bellevue, WA, USA
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Quelle 1

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Titel: 28th International Conference on Machine Learning (ICML 2011)
Genre der Quelle: Konferenzband
 Urheber:
Getoor, L, Herausgeber
Scheffer, T, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Madison, WI, USA : International Machine Learning Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 49 - 56 Identifikator: ISBN: 978-1-450-30619-5