论文标题
图增强了高维内核回归
Graph Enhanced High Dimensional Kernel Regression
论文作者
论文摘要
在本文中，内核回归的灵活性，多功能性和预测能力与现在富裕的网络数据相结合，以创建具有更大预测性能的回归模型。我们从以前的工作中建立了构建在网络内聚力数据存在下的广义线性模型，我们构建了一个内核扩展，该扩展位于极高的维空间中捕获微妙的非线性，并且还可以产生更好的预测性能。无缝但实质性适应对模拟和现实生活数据的应用表明了我们工作的吸引力和优势。
In this paper, the flexibility, versatility and predictive power of kernel regression are combined with now lavishly available network data to create regression models with even greater predictive performances. Building from previous work featuring generalized linear models built in the presence of network cohesion data, we construct a kernelized extension that captures subtler nonlinearities in extremely high dimensional spaces and also produces far better predictive performances. Applications of seamless yet substantial adaptation to simulated and real-life data demonstrate the appeal and strength of our work.
