论文标题
格格：自适应聚集，具有分布式特征选择的稳定性
ADAGES: adaptive aggregation with stability for distributed feature selection
论文作者
论文摘要
在这个“大”数据的时代，不仅大量数据一直在激励分布式计算，而且对数据隐私的担忧也提出了对分布式学习的重视。为了进行特征选择并以多机械或多种规定的分布式模式以分布式模式控制错误的发现率，必须采用有效的聚合方法。在本文中，我们提出了一种称为ADAGE的自适应聚合方法，可以灵活地应用于任何机器智能特征选择方法。我们将证明我们的方法能够以理论基础来控制整体FDR，同时保持与实践中的联盟聚合规则一样好。
In this era of "big" data, not only the large amount of data keeps motivating distributed computing, but concerns on data privacy also put forward the emphasis on distributed learning. To conduct feature selection and to control the false discovery rate in a distributed pattern with multi-machines or multi-institutions, an efficient aggregation method is necessary. In this paper, we propose an adaptive aggregation method called ADAGES which can be flexibly applied to any machine-wise feature selection method. We will show that our method is capable of controlling the overall FDR with a theoretical foundation while maintaining power as good as the Union aggregation rule in practice.
