# Boosting for Comparison-Based Learning

arXiv: https://arxiv.org/abs/1810.13333 · HTML: https://mlsanity.com/paper/1810.13333/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [A Revenue Function for Comparison-Based Hierarchical Clustering](https://mlsanity.com/paper/2211.16459/) (2022)

## Abstract (arXiv)

We consider the problem of classification in a comparison-based setting: given a set of objects, we only have access to triplet comparisons of the form "object xi is closer to object xj than to object xk." In this paper we introduce TripletBoost, a new method that can learn a classifier just from such triplet comparisons. The main idea is to aggregate the triplets information into weak classifiers, which can subsequently be boosted to a strong classifier. Our method has two main advantages: (i) it is applicable to data from any metric space, and (ii) it can deal with large scale problems using only passively obtained and noisy triplets. We derive theoretical generalization guarantees and a lower bound on the number of necessary triplets, and we empirically show that our method is both competitive with state of the art approaches and resistant to noise.