Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  Multivariate Regression with Stiefel Constraints

Bakir, G., Gretton, A., Franz, M., & Schölkopf, B.(2004). Multivariate Regression with Stiefel Constraints (128). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
MPIK-TR-128.pdf (Verlagsversion), 410KB
Name:
MPIK-TR-128.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Bakir, GH1, 2, Autor           
Gretton, A1, 2, Autor           
Franz, MO1, 2, Autor           
Schölkopf, B1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: We introduce a new framework for regression between multi-dimensional spaces. Standard methods for solving this problem typically reduce the problem to one-dimensional
regression by choosing features in the input and/or output spaces. These methods, which
include PLS (partial least squares), KDE (kernel dependency estimation), and PCR
(principal component regression), select features based on different a-priori judgments as
to their relevance. Moreover, loss function and constraints are chosen not primarily on
statistical grounds, but to simplify the resulting optimisation. By contrast, in our
approach the feature construction and the regression estimation are performed jointly,
directly minimizing a loss function that we specify, subject to a rank constraint. A
major advantage of this approach is that the loss is no longer chosen according to the
algorithmic requirements, but can be tailored to the characteristics of the task at hand;
the features will then be optimal with respect to this objective. Our approach also
allows for the possibility of using a regularizer in the optimization. Finally, by processing the observations sequentially, our algorithm is able to work on large scale problems.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2004-07
 Publikationsstatus: Erschienen
 Seiten: 25
 Ort, Verlag, Ausgabe: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: Reportnr.: 128
BibTex Citekey: 2831
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Max Planck Institute for Biological Cybernetics
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 128 Artikelnummer: - Start- / Endseite: - Identifikator: -