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.
翻译:在机器学习和信号处理的各种应用中,出现了非消极和可约束的线性回归问题。在本文件中,我们提出一种方法,通过在迭代过程中确定饱和坐标来加速这些问题的现有解决者。这类似于以前为宽度和正态回归问题提出的安全筛选技术。拟议的战略是相当安全的,因为它提供了理论保证,确认的坐标确实饱和在最佳解决办法中。合成和真实数据的实验结果显示,非负性和可约束性问题的加速。