OF-AE: Oblique Forest AutoEncoders

TL;DR

OF-AE introduces an unsupervised ensemble autoencoder using oblique trees with multivariate splits, extending previous methods by employing linear combinations of features for improved encoding.

Contribution

It presents a novel oblique forest-based autoencoder that leverages multivariate linear splits for unsupervised feature encoding.

Findings

Utilizes oblique splits for better feature representation.

Extends eForest encoder with oblique trees.

Provides open-source code for reproducibility.

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.