OF-AE: Oblique Forest AutoEncoders
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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.
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References · 12
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
- 2017Distilling a Neural Network Into a Soft Decision Treecited by 230
- 2017A Rewriting System for Convex Optimization Problemscited by 178
- 2013Generalized Denoising Auto-Encoders as Generative Modelscited by 113
- 2018Adaptive Neural Treescited by 64
- 2015HHCART: An Oblique Decision Treecited by 13
- 2020Learning Binary Decision Trees by Argmin Differentiationcited by 11
- 2017AutoEncoder by Forestcited by 8
- 2022Generative Trees: Adversarial and Copycatcited by 7
- 2014Autoencoder Treescited by 6
- 2018Probabilistic Random Forest: A machine learning algorithm for noisy datasetscited by 5
- 2015CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splitscited by 4
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