论文标题
成对监督可以证明引起决策界限
Pairwise Supervision Can Provably Elicit a Decision Boundary
论文作者
论文摘要
相似性学习是一个普遍的问题，可以通过预测一对模式之间的关系来引起有用的表示。这个问题与各种重要的预处理任务有关，例如公制学习，内核学习和对比度学习。基于表示形式的分类器有望在下游分类中表现良好。但是，到目前为止，文献中几乎没有理论，因此相似性和分类之间的关系仍然难以捉摸。因此，我们解决了一个基本问题：相似性信息可以证明可以导致模型在下游分类中表现良好吗？在本文中，我们揭示了相似性学习的产品类型公式与二元分类的目标密切相关。我们进一步表明，这两个不同的问题是通过多余的风险约束明确联系的。因此，我们的结果阐明了相似性学习能够通过直接引起决策边界来解决二进制分类。
Similarity learning is a general problem to elicit useful representations by predicting the relationship between a pair of patterns. This problem is related to various important preprocessing tasks such as metric learning, kernel learning, and contrastive learning. A classifier built upon the representations is expected to perform well in downstream classification; however, little theory has been given in literature so far and thereby the relationship between similarity and classification has remained elusive. Therefore, we tackle a fundamental question: can similarity information provably leads a model to perform well in downstream classification? In this paper, we reveal that a product-type formulation of similarity learning is strongly related to an objective of binary classification. We further show that these two different problems are explicitly connected by an excess risk bound. Consequently, our results elucidate that similarity learning is capable of solving binary classification by directly eliciting a decision boundary.
