论文标题
来自聚合标签的极端多标签分类
Extreme Multi-label Classification from Aggregated Labels
论文作者
论文摘要
极端的多标签分类（XMC）是从一个可能的标签宇宙中找到输入的相关标签的问题。我们在仅适用于样本组的标签中考虑XMC，但不适合单个样品。当前的XMC方法不是为这种多实体多标签（MIML）训练数据而构建的，并且MIML方法不能扩展到XMC大小。我们开发了一种新的可扩展算法，以从组标签中估算单个样本标签。可以将其与任何现有的XMC方法配对，以解决汇总标签问题。我们在轻度假设下表征了算法的统计特性，并为MIML提供了新的端到端框架作为扩展。汇总标签XMC和MIML任务的实验都显示出比现有方法的优点。
Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for groups of samples - but not for individual ones. Current XMC approaches are not built for such multi-instance multi-label (MIML) training data, and MIML approaches do not scale to XMC sizes. We develop a new and scalable algorithm to impute individual-sample labels from the group labels; this can be paired with any existing XMC method to solve the aggregated label problem. We characterize the statistical properties of our algorithm under mild assumptions, and provide a new end-to-end framework for MIML as an extension. Experiments on both aggregated label XMC and MIML tasks show the advantages over existing approaches.
