GMM Discriminant Analysis with Noisy Label for Each Class
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 49 references that are in the map. First posted on arXiv 25 Jan 2022.
Builds on
8 of 49 references.
References · 8
- 2020DivideMix: Learning with Noisy Labels as Semi-supervised Learningcited by 691
- 2019NLNL: Negative Learning for Noisy Labelscited by 151
- 2019Deep Self-Learning From Noisy Labelscited by 122
- 2015Learning with Symmetric Label Noise: The Importance of Being Unhingedcited by 120
- 2013Classification with Asymmetric Label Noise: Consistency and Maximal Denoisingcited by 67
- 2018DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Modelcited by 8
- 2015Learning in the Presence of Corruptioncited by 3
- 2019Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noisecited by 2
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