# OF-AE: Oblique Forest AutoEncoders

arXiv: https://arxiv.org/abs/2301.00880 · HTML: https://mlsanity.com/paper/2301.00880/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [A Rewriting System for Convex Optimization Problems](https://mlsanity.com/paper/1709.04494/) (2017)
- [Generalized Denoising Auto-Encoders as Generative Models](https://mlsanity.com/paper/1305.6663/) (2013)
- [Adaptive Neural Trees](https://mlsanity.com/paper/1807.06699/) (2018)
- [HHCART: An Oblique Decision Tree](https://mlsanity.com/paper/1504.03415/) (2015)
- [Learning Binary Decision Trees by Argmin Differentiation](https://mlsanity.com/paper/2010.04627/) (2020)
- [AutoEncoder by Forest](https://mlsanity.com/paper/1709.09018/) (2017)
- [Generative Trees: Adversarial and Copycat](https://mlsanity.com/paper/2201.11205/) (2022)
- [Autoencoder Trees](https://mlsanity.com/paper/1409.7461/) (2014)
- [Probabilistic Random Forest: A machine learning algorithm for noisy datasets](https://mlsanity.com/paper/1811.05994/) (2018)
- [CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits](https://mlsanity.com/paper/1506.06155/) (2015)

## Abstract (arXiv)

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.