# Discriminative Metric Learning with Deep Forest

## TL;DR

This paper introduces Discriminative Deep Forest, a metric learning algorithm that assigns weights to decision trees to improve class separation, demonstrated through numerical experiments.

## Contribution

It proposes a novel metric learning method based on Deep Forest with weighted trees and a simplified optimization objective.

## Key findings

- Effective in reducing intra-class distances
- Increases inter-class distances
- Demonstrated through numerical experiments

## Abstract

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.