标题： 决策导向数据分解 显示英文标题

标题： Decision-Directed Data Decomposition

摘要： 我们提出了一种名为决策导向数据分解（D4）的算法，该算法将数据集分解为两个部分。第一个部分包含了指定监督学习任务所需大部分有用信息；第二个正交部分关于该任务包含的信息很少，但保留了未针对的目标关联和信息。该算法简单且可扩展。我们在图像和文本处理领域展示了它的应用实例。我们的结果显示：1）在图像表示空间后验应用D4可以去除指定概念的信息而不影响其他概念；2）在某些情况下，D4能够提升预测泛化能力；3）对词嵌入表示应用D4可以在去偏见方面产生最先进的结果。 显示英文摘要

摘要： We present an algorithm, Decision-Directed Data Decomposition (D4), which decomposes a dataset into two components. The first contains most of the useful information for a specified supervised learning task. The second orthogonal component contains little information about the task but retains associations and information that were not targeted. The algorithm is simple and scalable. We illustrate its application in image and text processing domains. Our results show that 1) post-hoc application of D4 to an image representation space can remove information about specified concepts without impacting other concepts, 2) D4 is able to improve predictive generalization in certain settings, and 3) applying D4 to word embedding representations produces state-of-the-art results in debiasing.