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。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第17期175-177,共3页
Computer Engineering
基金
国家自然科学基金资助项目"基于损失函数的统计机器学习算法及其应用研究"(60975040)
关键词
L1正则化
机器学习
稀疏性
多阶段
多步骤
L1 regularized
machine learning
sparsity
multi-stage
multi-step
