论文标题
具有特征功能增强的图基函数的图形上的半监督学习
Semi-Supervised Learning on Graphs with Feature-Augmented Graph Basis Functions
论文作者
论文摘要
对于在图表上进行半监督的学习，我们研究如何使用已知先验的其他知识或无监督学习输出的其他信息来增强监督学习方案中的初始内核。这些增强核是在基于内核的Schur-Hadamard产品的简单更新方案中构建的，并具有其他功能核。作为正定核的发电机，我们将重点关注图基函数（GBF），这些函数允许通过图傅立叶变换来包含图形的几何信息。使用用于机器学习的正则最小二乘方法（RLS）方法，我们将测试派生的增强核，以分类图上的数据。
For semi-supervised learning on graphs, we study how initial kernels in a supervised learning regime can be augmented with additional information from known priors or from unsupervised learning outputs. These augmented kernels are constructed in a simple update scheme based on the Schur-Hadamard product of the kernel with additional feature kernels. As generators of the positive definite kernels we will focus on graph basis functions (GBF) that allow to include geometric information of the graph via the graph Fourier transform. Using a regularized least squares (RLS) approach for machine learning, we will test the derived augmented kernels for the classification of data on graphs.
