Regression with Linear Factored Functions
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.
Cite
Text
Böhmer and Obermayer. "Regression with Linear Factored Functions." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23528-8_8Markdown
[Böhmer and Obermayer. "Regression with Linear Factored Functions." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/bohmer2015ecmlpkdd-regression/) doi:10.1007/978-3-319-23528-8_8BibTeX
@inproceedings{bohmer2015ecmlpkdd-regression,
title = {{Regression with Linear Factored Functions}},
author = {Böhmer, Wendelin and Obermayer, Klaus},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2015},
pages = {119-134},
doi = {10.1007/978-3-319-23528-8_8},
url = {https://mlanthology.org/ecmlpkdd/2015/bohmer2015ecmlpkdd-regression/}
}