@incollection{Boehmer15b,
Title = {Regression with Linear Factored Functions},
Author = {B\"ohmer, W. and Obermayer, K.},
Booktitle = {Machine Learning and Knowledge Discovery in Databases},
Pages = {119-134},
Year = {2015},
Isbn = {978-3-319-23527-1, 978-3-319-23528-8},
Issn = {0302-9743},
Doi = {10.1007/978-3-319-23528-8_8},
Volume = {9284},
Publisher = {Springer International Publishing},
Series = {Lecture Notes in Computer Science},
Abstract = {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.},
Url = {http://www.redaktion.tu-berlin.de/fileadmin/fg215/articles/boehmer2015b.pdf},
Projectname = {Machine Learning}
}