偿还元数据债务：使用主题模型学习概念的表示
Paying down metadata debt: learning the representation of concepts using topic models
摘要：
我们引入了一个称为元数据债的数据管理问题，以识别数据概念及其逻辑表示之间的映射。我们描述了如何使用基于低秩矩阵分解的半监督主题模型来学习这种映射，该模型考虑了缺失和噪声标签，再加上稀疏惩罚以提高本地化和可解释性。我们引入了一个规范转换的方法，使我们能够构建明确的主题和概念标签之间的关联，从而赋予主题的意义。我们还展示了如何使用这个主题模型进行半监督学习任务，例如从已知标签中推断，评估现有标签中可能的错误，以及预测缺失的特征。我们展示了这个主题模型在Kaggle.com的25，000多个数据集上预测主题标签的结果，展示了学习语义有意义特征的能力。
英文摘要：
We introduce a data management problem called metadata debt, to identify the mapping between data concepts and their logical representations. We describe how this mapping can be learned using semisupervised topic models based on low-rank matrix factorizations that account for missing and noisy labels, coupled with sparsity penalties to improve localization and interpretability. We introduce a gauge transformation approach that allows us to construct explicit associations between topics and concept labels, and thus assign meaning to topics. We also show how to use this topic model for semisupervised learning tasks like extrapolating from known labels, evaluating possible errors in existing labels, and predicting missing features. We show results from this topic model in predicting subject tags on over 25,000 datasets from Kaggle.com, demonstrating the ability to learn semantically meaningful features.
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