Faster and Still Safe: Combining Screening Techniques and Structured Dictionaries to Accelerate the Lasso

C. Dantas, R. Gribonval
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引用次数: 6

Abstract

Accelerating the solution of the Lasso problem becomes crucial when scaling to very high dimensional data. In this paper, we propose a way to combine two existing acceleration techniques: safe screening tests, which simplify the problem by eliminating useless dictionary atoms; and the use of structured dictionaries which are faster to operate with. A structured approximation of the true dictionary is used at the initial stage of the optimization, and we show how to define screening tests which are still safe despite the approximation error. In particular, we extend a state-of-the-art screening test, the GAP SAFE sphere test, to this new setting. The practical interest of the proposed methodology is demonstrated by considerable reductions in simulation time.
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更快,仍然安全:结合筛选技术和结构化词典加速套索
当缩放到非常高维的数据时,加速Lasso问题的解决变得至关重要。在本文中,我们提出了一种结合两种现有加速技术的方法:安全筛选测试,它通过消除无用的字典原子来简化问题;使用结构化字典,操作起来更快。在优化的初始阶段使用了真实字典的结构化近似,并展示了如何定义尽管存在近似误差但仍然安全的筛选测试。特别是,我们将最先进的筛选测试,GAP安全球体测试,扩展到这个新环境。所提出的方法的实际意义是通过大大减少模拟时间来证明的。
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