摘要： 以稀疏学习为主线，从多阶段、多步骤优化思想的角度出发，对当前流行的L1正则化求解算法进行分类，比较基于次梯度的多步骤方法、基于坐标优化的多阶段方法，以及软L1正则化方法的收敛性能、时空复杂度和解的稀疏程度。分析表明，基于机器学习问题特殊结构的学习算法可以获得较好的稀疏性和较快的收敛速度。

关键词: L1正则化, 机器学习, 稀疏性, 多阶段, 多步骤

Abstract: To deal with the new time and space challenges of the machine learning problem algorithms from large scale data, this paper focuses on sparse-learning and categorizes the L1 regularized problem’s the-state-of-the-art solvers from the view of multi-stage and multi-step optimization schemes. It compares the algorithms’ convergence properties, time and space cost and the sparsity of these solvers. The analysis shows that those algorithms sufficiently exploiting the machine learning problem’s specific structure obtain better sparsity as well as faster convergence rate.

Key words: L1 regularized, machine learning, sparsity, multi-stage, multi-step

中图分类号: 

• TP181