PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multivariate Regression via Stiefel Constraints
Goekhan BakIr, Arthur Gretton, Matthias Franz and Bernhard Schölkopf
In: DAGM 2004, Aug 30-Sep 01 2004, Tuebingen, Germany.


We introduce a learning technique for regression between high-dimensional spaces. Standard methods typically reduce this task to many one-dimensional problems, with each output dimension considered independently. 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, and dependence between the outputs can be exploited.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:589
Deposited By:Arthur Gretton
Deposited On:26 December 2004