提出安全筛选规则，加速高维多任务学习的特征选择。
Safe Screening Rules for Group OWL Models
- 针对结构化非可分正则项，设计可安全应用的特征筛选规则。
- 实验显示能准确识别零系数特征，显著提升计算速度且不损失精度。
- 适用于批量与随机优化，可无缝集成现有求解器，适合大规模学习场景。
Group Ordered Weighted $L_{1}$-Norm（Group OWL）正则化模型在高维稀疏多任务学习中表现优异，尤其适用于特征相关的情况。目前主流使用近端梯度法求解，但当特征数量庞大时，计算成本和内存开销巨大。本文首次为Group OWL模型提出安全筛选规则，有效处理结构化非可分惩罚项，能够快速识别所有任务中系数为零的无关特征。通过在训练过程中剔除这些无效特征，可实现显著的计算效率提升与内存节省。更重要的是，该筛选规则可直接嵌入批处理与随机优化的现有求解器中。理论上，我们证明了该规则的安全性，并可安全应用于各类迭代优化算法。实验结果表明，该规则能有效识别无关特征，在不牺牲准确性的前提下带来显著的计算加速。
原文摘要 · Abstract (English)
Group Ordered Weighted $L_{1}$-Norm (Group OWL) regularized models have emerged as a useful procedure for high-dimensional sparse multi-task learning with correlated features. Proximal gradient methods are used as standard approaches to solving Group OWL models. However, Group OWL models usually suffer huge computational costs and memory usage when the feature size is large in the high-dimensional scenario. To address this challenge, in this paper, we are the first to propose the safe screening rule for Group OWL models by effectively tackling the structured non-separable penalty, which can quickly identify the inactive features that have zero coefficients across all the tasks. Thus, by removing the inactive features during the training process, we may achieve substantial computational gain and memory savings. More importantly, the proposed screening rule can be directly integrated with the existing solvers both in the batch and stochastic settings. Theoretically, we prove our screening rule is safe and also can be safely applied to the existing iterative optimization algorithms. Our experimental results demonstrate that our screening rule can effectively identify the inactive features and leads to a significant computational speedup without any loss of accuracy.
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