论文标题
高维山脊回归的最佳子采样
Optimal Subsampling for High-dimensional Ridge Regression
论文作者
论文摘要
我们使用最佳亚采样技术研究了高维脊回归的特征压缩。具体而言，基于Ridge回归和A-最佳设计标准的特征随机采样算法的基本框架，我们首先获得一组最佳的亚采样概率。考虑到获得的概率是不经济的，我们然后提出了几乎最佳的概率。有了这些概率，建立了两步的迭代算法，该算法的计算成本较低，准确性较高。我们提供理论分析和数值实验，以支持所提出的方法。数值结果证明了我们方法的不错性能。
We investigate the feature compression of high-dimensional ridge regression using the optimal subsampling technique. Specifically, based on the basic framework of random sampling algorithm on feature for ridge regression and the A-optimal design criterion, we first obtain a set of optimal subsampling probabilities. Considering that the obtained probabilities are uneconomical, we then propose the nearly optimal ones. With these probabilities, a two step iterative algorithm is established which has lower computational cost and higher accuracy. We provide theoretical analysis and numerical experiments to support the proposed methods. Numerical results demonstrate the decent performance of our methods.
