  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.

Machine Learning