论文标题
带有安全筛选的加速非负和有限变量的线性回归算法
Accelerating Non-Negative and Bounded-Variable Linear Regression Algorithms with Safe Screening
论文作者
论文摘要
在机器学习和信号处理中的多种应用中，出现了非负和有限变量的线性回归问题。在本文中，我们提出了一种通过在迭代过程中识别饱和坐标来加速现有求解器的技术。这类似于先前针对稀疏性调查回归问题的安全筛查技术。提出的策略证明是安全的，因为它提供了理论上的保证，即确定的坐标确实在最佳解决方案中饱和。合成和真实数据的实验结果表明，非负和可变性问题的引人入胜的加速度。
Non-negative and bounded-variable linear regression problems arise in a variety of applications in machine learning and signal processing. In this paper, we propose a technique to accelerate existing solvers for these problems by identifying saturated coordinates in the course of iterations. This is akin to safe screening techniques previously proposed for sparsity-regularized regression problems. The proposed strategy is provably safe as it provides theoretical guarantees that the identified coordinates are indeed saturated in the optimal solution. Experimental results on synthetic and real data show compelling accelerations for both non-negative and bounded-variable problems.
