论文标题
随机功能的无骑行回归
Ridgeless Regression with Random Features
论文作者
论文摘要
最近的理论研究表明，内核无脊回归可以保证无明确正规化的良好概括能力。在本文中，我们研究了具有随机特征和随机梯度下降的无乘式回归的统计特性。我们分别探讨了随机梯度和随机特征中因素的影响。具体而言，随机特征误差表现出双研究曲线。在理论发现的激励下，我们提出了一种可调核算法，该算法优化了训练过程中内核的光谱密度。我们的工作桥接了插值理论和实用算法。
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization. In this paper, we investigate the statistical properties of ridgeless regression with random features and stochastic gradient descent. We explore the effect of factors in the stochastic gradient and random features, respectively. Specifically, random features error exhibits the double-descent curve. Motivated by the theoretical findings, we propose a tunable kernel algorithm that optimizes the spectral density of kernel during training. Our work bridges the interpolation theory and practical algorithm.
