论文标题
具有自适应子空间采样的近端梯度方法
Proximal Gradient methods with Adaptive Subspace Sampling
论文作者
论文摘要
机器学习或信号处理中的许多应用都涉及非平滑优化问题。这种非平滑度为最佳解决方案带来了低维结构。在本文中，我们提出了一种利用这种基本结构的随机近端梯度方法。我们介绍了两个关键组件：i）随机子空间近端梯度算法； ii）基于标识的子空间采样。他们的相互作用在探索的维度方面对典型的学习问题产生了重大的绩效改善。
Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions. In this paper, we propose a randomized proximal gradient method harnessing this underlying structure. We introduce two key components: i) a random subspace proximal gradient algorithm; ii) an identification-based sampling of the subspaces. Their interplay brings a significant performance improvement on typical learning problems in terms of dimensions explored.
