ausblenden:
Schlagwörter:
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Zusammenfassung:
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.