# Discriminative Metric Learning with Deep Forest

arXiv: https://arxiv.org/abs/1705.09620 · HTML: https://mlsanity.com/paper/1705.09620/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2017.

## Built on by (most cited first, 3 of 3)

- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)
- [Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning](https://mlsanity.com/paper/2407.16091/) (2024)
- [The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest](https://mlsanity.com/paper/2407.05108/) (2024)

## Abstract (arXiv)

A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. The case of the fully supervised learning is studied when the class labels of individual training examples are known. The main idea underlying the algorithm is to assign weights to decision trees in random forest in order to reduce distances between objects from the same class and to increase them between objects from different classes. The weights are training parameters. A specific objective function which combines Euclidean and Manhattan distances and simplifies the optimization problem for training the DisDF is proposed. The numerical experiments illustrate the proposed distance metric algorithm.