Wavelet decompositions of Random Forests - smoothness analysis, sparse approximation and applications

@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}
}
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

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