论文标题
GMM判别分析，每个班级都有嘈杂的标签
GMM Discriminant Analysis with Noisy Label for Each Class
论文作者
论文摘要
现实世界的数据集通常包含嘈杂的标签，并且使用标准分类方法从此类数据集中学习可能不会产生所需的性能。在本文中，我们提出了每个班级的高斯混合物判别分析（GMDA）。我们引入了翻转概率和类概率，并使用EM算法来解决标签噪声的判别问题。我们还提供了融合的详细证明。合成和现实世界数据集的实验结果表明，所提出的方法尤其优于其他四个最新方法。
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.
