论文标题
分布式稀疏多材判别分析
Distributed Sparse Multicategory Discriminant Analysis
论文作者
论文摘要
本文提出了用于稀疏多级线性判别分析的凸公式，然后在数据跨多个站点存储时将其扩展到分布式设置。关键观察结果是，出于分类目的，恢复正交转换不变的判别子空间足够了。从理论上讲，我们建立统计属性，以确保分布式稀疏多级线性判别分析的性能与{几轮通信}后的集中式版本一样好。数值研究为我们的方法和理论提供了强有力的支持。
This paper proposes a convex formulation for sparse multicategory linear discriminant analysis and then extend it to the distributed setting when data are stored across multiple sites. The key observation is that for the purpose of classification it suffices to recover the discriminant subspace which is invariant to orthogonal transformations. Theoretically, we establish statistical properties ensuring that the distributed sparse multicategory linear discriminant analysis performs as good as the centralized version after {a few rounds} of communications. Numerical studies lend strong support to our methodology and theory.
