# Hierarchical Quadratic Random Forest Classifier

arXiv: https://arxiv.org/abs/2306.01893 · HTML: https://mlsanity.com/paper/2306.01893/

## Key facts

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

## Builds on (most cited first, 2 of 2)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Multiclass Sparse Discriminant Analysis](https://mlsanity.com/paper/1504.05845/) (2015)

## Abstract (arXiv)

In this paper, we proposed a hierarchical quadratic random forest classifier for classifying multiresolution samples extracted from multichannel data. This forest incorporated a penalized multivariate linear discriminant in each of its decision nodes and processed squared features to realize quadratic decision boundaries in the original feature space. The penalized discriminant was based on a multiclass sparse discriminant analysis and the penalization was based on a group Lasso regularizer which was an intermediate between the Lasso and the ridge regularizer. The classification probabilities estimated by this forest and the features learned by its decision nodes could be used standalone or foster graph-based classifiers.