Label noise is known to negatively impact the performance of classification algorithms. In this paper, we develop a model robust to label noise that uses both labelled and unlabelled samples. In particular, we propose a novel algorithm to optimize the model parameters that scales efficiently w.r.t. the number of training samples. Our contribution relies on a consensus formulation of the original objective function that is highly parallelizable. The optimization is performed with the Alternating Direction Method of Multipliers framework. Experimental results on synthetic datasets show an improvement of several orders of magnitude in terms of processing time, with no loss in terms of accuracy. Our method appears also tailored to handle real data with significant label noise.

众所周知，标签噪声会对分类算法的性能产生负面影响。在本文中，我们开发了一个鲁棒的模型来标记噪声，该模型同时使用标记和未标记样本。特别是，我们提出了一种新算法来优化模型参数，该算法可以有效地扩展训练样本的数量。我们的贡献依赖于高度并行化的原始目标函数的共识公式。优化是使用乘法器框架的交替方向方法执行的。合成数据集的实验结果表明，处理时间提高了几个数量级，而准确性没有损失。我们的方法似乎也适合处理具有显着标签噪声的真实数据。