Highly Influenced

15 Excerpts

@article{Elisha2016WaveletDO, title={Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications}, author={Oren Elisha and Shai Dekel}, journal={Journal of Machine Learning Research}, year={2016}, volume={17}, pages={198:1-198:38} }

- Published in Journal of Machine Learning Research 2016

In this paper we introduce, in the setting of machine learning, a generalization of wavelet analysis which is a popular approach to low dimensional structured signal analysis. The wavelet decomposition of a Random Forest provides a sparse approximation of any regression or classification high dimensional function at various levels of detail, with a concrete ordering of the Random Forest nodes: from ‘significant’ elements to nodes capturing only ‘insignificant’ noise. Motivated by function space… CONTINUE READING