论文标题
锚定正规化的快速本地线性回归
Fast local linear regression with anchor regularization
论文作者
论文摘要
回归是机器学习和数据挖掘的重要任务。它在各个领域都有多个应用，包括金融，生物医学和计算机视觉。最近，提出了通过使用网络信息制作集群来估算本地模型的网络拉索，并展示了其出色的性能。在这项研究中，我们提出了一种简单而有效的本地模型训练算法，称为快速锚定局部线性方法（秋季）。更具体地说，我们通过使用预先计算的锚模型将每个样本正规化每个样本的本地模型。所提出的算法的关键优势在于，我们只能获得具有矩阵乘法的封闭形式解决方案。另外，提出的算法很容易解释，可以快速计算和并行化。通过对合成和现实世界数据集的实验，我们证明，秋天的准确性与最先进的网络套索算法相比，具有明显较小的训练时间（两个数量级）。
Regression is an important task in machine learning and data mining. It has several applications in various domains, including finance, biomedical, and computer vision. Recently, network Lasso, which estimates local models by making clusters using the network information, was proposed and its superior performance was demonstrated. In this study, we propose a simple yet effective local model training algorithm called the fast anchor regularized local linear method (FALL). More specifically, we train a local model for each sample by regularizing it with precomputed anchor models. The key advantage of the proposed algorithm is that we can obtain a closed-form solution with only matrix multiplication; additionally, the proposed algorithm is easily interpretable, fast to compute and parallelizable. Through experiments on synthetic and real-world datasets, we demonstrate that FALL compares favorably in terms of accuracy with the state-of-the-art network Lasso algorithm with significantly smaller training time (two orders of magnitude).
