# GMM Discriminant Analysis with Noisy Label for Each Class

arXiv: https://arxiv.org/abs/2201.10242 · HTML: https://mlsanity.com/paper/2201.10242/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [NLNL: Negative Learning for Noisy Labels](https://mlsanity.com/paper/1908.07387/) (2019)
- [Deep Self-Learning From Noisy Labels](https://mlsanity.com/paper/1908.02160/) (2019)
- [Learning with Symmetric Label Noise: The Importance of Being Unhinged](https://mlsanity.com/paper/1505.07634/) (2015)
- [Classification with Asymmetric Label Noise: Consistency and Maximal Denoising](https://mlsanity.com/paper/1303.1208/) (2013)
- [DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model](https://mlsanity.com/paper/1808.09211/) (2018)
- [Learning in the Presence of Corruption](https://mlsanity.com/paper/1504.00091/) (2015)
- [Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise](https://mlsanity.com/paper/1911.01855/) (2019)

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

- [Robust Noisy Label Learning via Two-Stream Sample Distillation](https://mlsanity.com/paper/2404.10499/) (2024)

## Abstract (arXiv)

Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance. In this paper, we propose a Gaussian Mixture Discriminant Analysis (GMDA) with noisy label for each class. We introduce flipping probability and class probability and use EM algorithms to solve the discriminant problem with label noise. We also provide the detail proofs of convergence. Experimental results on synthetic and real-world datasets show that the proposed approach notably outperforms other four state-of-art methods.