论文标题
班级事先估计在协变量偏移下：没问题？
Class Prior Estimation under Covariate Shift: No Problem?
论文作者
论文摘要
我们表明，在分类的背景下，如果减少了协变量中捕获的信息内容，则可能会丢失源和目标分布的属性和目标分布，例如，通过删除组件或映射到较低维度或有限的空间中。结果，在协变性的情况下，简单的方法是对分类风格的班级先验估计，而在有或没有调整的情况下进行计数是不可行的。我们证明，在整个协变量的统计意义上，保留协变量转移特性的协变量的转换必须足够。提出了探测算法作为协变量转移下的班级先验估计的替代方法。
We show that in the context of classification the property of source and target distributions to be related by covariate shift may be lost if the information content captured in the covariates is reduced, for instance by dropping components or mapping into a lower-dimensional or finite space. As a consequence, under covariate shift simple approaches to class prior estimation in the style of classify and count with or without adjustment are infeasible. We prove that transformations of the covariates that preserve the covariate shift property are necessarily sufficient in the statistical sense for the full set of covariates. A probing algorithm as alternative approach to class prior estimation under covariate shift is proposed.
