English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  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

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-F347-E Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8B2E-F
Genre: Report

Files

show Files
hide Files
:
MPIK-TR-128.pdf (Publisher version), 410KB
Name:
MPIK-TR-128.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Bakir, GH1, 2, Author              
Gretton, A1, 2, Author              
Franz, MO1, 2, Author              
Schölkopf, B1, 2, Author              
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              

Content

show
hide
Free keywords: -
 Abstract: 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

show
hide
Language(s):
 Dates: 2004-07
 Publication Status: Published in print
 Pages: 25
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Method: -
 Identifiers: Report Nr.: 128
BibTex Citekey: 2831
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Max Planck Institute for Biological Cybernetics
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 128 Sequence Number: - Start / End Page: - Identifier: -