拓展分布偏移分析框架，支持分类回归通用场景。
Factorizable joint shift revisited
- 提出适用于任意标签空间的分布偏移分析框架
- 将现有因子化联合偏移理论推广至回归等通用标签场景
- 为广义标签偏移与期望最大化算法提供新视角
因子化联合偏移（FJS）是一种同时包含协变量偏移和标签偏移的分布偏移类型。近期研究发现，FJS 实际上源于连续的标签偏移与协变量偏移（或反之）。以往对 FJS 的研究主要局限于类别标签情形。本文提出一个适用于一般标签空间的分布偏移分析框架，涵盖分类与回归模型。基于该框架，我们将现有 FJS 理论推广至一般标签空间，并对用于类别先验估计的期望最大化（EM）算法进行相关扩展与分析。此外，我们重新审视了在一般标签空间下的广义标签偏移（GLS）问题。
原文摘要 · Abstract (English)
Factorizable joint shift (FJS) represents a type of distribution shift (or dataset shift) that comprises both covariate and label shift. Recently, it has been observed that FJS actually arises from consecutive label and covariate (or vice versa) shifts. Research into FJS so far has been confined mostly to the case of categorical labels. We propose a framework for analysing distribution shift in the case of a general label space, thus covering both classification and regression models. Based on the framework, we generalise existing results on FJS to general label spaces and present and analyse a related extension to label distribution estimation of the expectation maximisation (EM) algorithm for class prior probabilities. We also take a fresh look at generalized label shift (GLS) in the case of a general label space.
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