# D'ecouvrir de nouvelles classes dans des donn'ees tabulaires

arXiv: https://arxiv.org/abs/2211.16352 · HTML: https://mlsanity.com/paper/2211.16352/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [Learning to Discover Novel Visual Categories via Deep Transfer Clustering](https://mlsanity.com/paper/1908.09884/) (2019)
- [Learning to cluster in order to transfer across domains and tasks](https://mlsanity.com/paper/1711.10125/) (2017)
- [Neighborhood Contrastive Learning for Novel Class Discovery](https://mlsanity.com/paper/2106.10731/) (2021)
- [AutoNovel: Automatically Discovering and Learning Novel Visual Categories](https://mlsanity.com/paper/2106.15252/) (2021)
- [A Closer Look at Novel Class Discovery from the Labeled Set](https://mlsanity.com/paper/2209.09120/) (2022)
- [From Weakly Supervised Learning to Biquality Learning: an Introduction](https://mlsanity.com/paper/2012.09632/) (2020)
- [A Method for Discovering Novel Classes in Tabular Data](https://mlsanity.com/paper/2209.01217/) (2022)

## Abstract (arXiv)

In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data. We show a way to extract knowledge from already known classes to guide the discovery process of novel classes in the context of tabular data which contains heterogeneous variables. A part of this process is done by a new method for defining pseudo labels, and we follow recent findings in Multi-Task Learning to optimize a joint objective function. Our method demonstrates that NCD is not only applicable to images but also to heterogeneous tabular data.