To alleviate the data requirement for training effective binary classifiers in binary classification, many weakly supervised learning settings have been proposed. Among them, some consider using pairwise but not pointwise labels, when pointwise labels are not accessible due to privacy, confidentiality, or security reasons. However, as a pairwise label denotes whether or not two data points share a pointwise label, it cannot be easily collected if either point is equally likely to be positive or negative. Thus, in this paper, we propose a novel setting called pairwise comparison (Pcomp) classification, where we have only pairs of unlabeled data that we know one is more likely to be positive than the other. Firstly, we give a Pcomp data generation process, derive an unbiased risk estimator (URE) with theoretical guarantee, and further improve URE using correction functions. Secondly, we link Pcomp classification to noisy-label learning to develop a progressive URE and improve it by imposing consistency regularization. Finally, we demonstrate by experiments the effectiveness of our methods, which suggests Pcomp is a valuable and practically useful type of pairwise supervision besides the pairwise label.
翻译:为了减轻在二进制分类中培训有效的二进制分类人员的数据要求,提出了许多监督不力的学习设置,其中有些国家考虑使用对称标签,但并非点性标签,因为由于隐私、保密或安全原因,点性标签无法进入。然而,作为对称标签表示两个数据点是否共用一个点性标签,如果这两个点都同样可能呈正或负,则无法轻易地收集这些数据。因此,在本文件中,我们提议了一个称为对称比较(Pcomp)分类的新设置,其中我们只有一对我们所知道的未贴标签数据比另一类数据更可能呈阳性。首先,我们给一个相配制数据生成过程,用理论保证产生一个不带偏见的风险估计符(URE),并用校正功能进一步改进URE。第二,我们将扰动标签分类与培养进步的URE学习联系起来,并通过强制一致性规范来改进它。最后,我们通过实验我们的方法的有效性,我们证明我们的方法是有价值的、实际有用的和有用的监督类型,除了配对标签之外。