OF-AE: Oblique Forest AutoEncoders

Cristian Daniel Alecsa
1/2/2023
cs.LG

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Abstract

In the present work we propose an unsupervised ensemble method consisting of oblique trees that can address the task of auto-encoding, namely Oblique Forest AutoEncoders (briefly OF-AE). Our method is a natural extension of the eForest encoder introduced in [1]. More precisely, by employing oblique splits consisting in multivariate linear combination of features instead of the axis-parallel ones, we will devise an auto-encoder method through the computation of a sparse solution of a set of linear inequalities consisting of feature values constraints. The code for reproducing our results is available at https://github.com/CDAlecsa/Oblique-Forest-AutoEncoders.

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Cite this paper

@article{alecsa2023ofae,
  title  = {OF-AE: Oblique Forest AutoEncoders},
  author = {Cristian Daniel Alecsa},
  year   = {2023},
  eprint = {2301.00880},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2301.00880v1}
}

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