OF-AE: Oblique Forest AutoEncoders
Machine Learning
2023-01-04 v1
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.
Cite
@article{arxiv.2301.00880,
title = {OF-AE: Oblique Forest AutoEncoders},
author = {Cristian Daniel Alecsa},
journal= {arXiv preprint arXiv:2301.00880},
year = {2023}
}
Comments
11 pages, 12 figures, 2 tables