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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Regression with Linear Factored Functions
verfasst von : Wendelin Böhmer, Klaus Obermayer
Erschienen in: Machine Learning and Knowledge Discovery in Databases
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Many applications that use empirically estimated functions face a
curse of dimensionality
, because integrals over most function classes must be approximated by sampling. This paper introduces a novel
regression
-algorithm that learns
linear factored functions
(LFF). This class of functions has structural properties that allow to analytically solve certain integrals and to calculate point-wise products. Applications like
belief propagation
and
reinforcement learning
can exploit these properties to break the curse and speed up computation. We derive a regularized greedy optimization scheme, that learns factored basis functions during training. The novel regression algorithm performs competitively to
Gaussian processes
on benchmark tasks, and the learned LFF functions are with 4-9 factored basis functions on average very compact.