用单参数岭回归解决极端多标签学习，简单高效且性能不输复杂模型。
A Simple but Effective Closed-form Solution for Extreme Multi-label Learning
- 基于岭回归构建闭式解方法，仅需一个超参数。
- 在多个基准数据集上表现媲美甚至超越复杂模型，低频标签预测显著提升。
- 通过频率加权策略改进低频标签预测，实现零修改即可增效。
极端多标签学习（XML）旨在为每个数据实例从海量标签中分配多个标签。当前高性能模型通常包含大量超参数，调参复杂且难以复现。为此，本文提出一种基于岭回归的简单方法，具有闭式解且仅含一个超参数。由于此前无将岭回归应用于XML的先例，本文在多个XML基准数据集上验证了其性能。此外，针对低频标签信息丰富但数据稀缺的挑战，采用简单的频率加权策略提升预测效果。实验表明，该方法性能可与多超参数模型比肩，甚至更优；频率加权显著改善低频标签预测，且几乎无需改动实现。源代码已开源：https://github.com/cars1015/XML-ridge。
原文摘要 · Abstract (English)
Extreme multi-label learning (XML) is a task of assigning multiple labels from an extremely large set of labels to each data instance. Many current high-performance XML models are composed of a lot of hyperparameters, which complicates the tuning process. Additionally, the models themselves are adapted specifically to XML, which complicates their reimplementation. To remedy this problem, we propose a simple method based on ridge regression for XML. The proposed method not only has a closed-form solution but also is composed of a single hyperparameter. Since there are no precedents on applying ridge regression to XML, this paper verified the performance of the method by using various XML benchmark datasets. Furthermore, we enhanced the prediction of low-frequency labels in XML, which hold informative content. This prediction is essential yet challenging because of the limited amount of data. Here, we employed a simple frequency-based weighting. This approach greatly simplifies the process compared with existing techniques. Experimental results revealed that it can achieve levels of performance comparable to, or even exceeding, those of models with numerous hyperparameters. Additionally, we found that the frequency-based weighting significantly improved the predictive performance for low-frequency labels, while requiring almost no changes in implementation. The source code for the proposed method is available on github at https://github.com/cars1015/XML-ridge.
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