# Unsupervised collaborative learning using privileged information

arXiv: https://arxiv.org/abs/2103.13145 · HTML: https://mlsanity.com/paper/2103.13145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 20 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Abstract (arXiv)

In the collaborative clustering framework, the hope is that by combining several clustering solutions, each one with its own bias and imperfections, one will get a better overall solution. The goal is that each local computation, quite possibly applied to distinct data sets, benefits from the work done by the other collaborators. This article is dedicated to collaborative clustering based on the Learning Using Privileged Information paradigm. Local algorithms weight incoming information at the level of each observation, depending on the confidence level of the classification of that observation. A comparison between our algorithm and state of the art implementations shows improvement of the collaboration process using the proposed approach.