Abstract

Hierarchical classification problems are commonly seen in practice. However, most existing methods do not fully use the hierarchical information among class labels. In this article, a novel label embedding approach is proposed, which keeps the hierarchy of labels exactly, and reduces the complexity of the hypothesis space significantly. Based on the newly proposed label embedding approach, a new angle-based classifier is developed for hierarchical classification. Moreover, to handle massive data, a new (weighted) linear loss is designed, which has a closed form solution and is computationally efficient. Theoretical properties of the new method are established and intensive numerical comparisons with other methods are conducted. Both simulations and applications in document categorization demonstrate the advantages of the proposed method. Supplementary materials for this article are available online.

摘要

层次分类问题在实践中很常见。然而，大多数现有方法都没有充分利用类标签之间的层次信息。在本文中，提出了一种新颖的标签嵌入方法，可以准确地保持标签的层次结构，并显着降低假设空间的复杂性。基于新提出的标签嵌入方法，开发了一种新的基于角度的分类器用于层次分类。此外，为了处理海量数据，设计了一种新的（加权）线性损失，它具有封闭形式的解决方案并且计算效率很高。建立了新方法的理论特性，并与其他方法进行了深入的数值比较。文档分类中的模拟和应用都证明了该方法的优点。