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.

中文翻译：

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具有特征增强图基函数的图的半监督学习

对于图上的半监督学习，我们研究如何使用来自已知先验或无监督学习输出的附加信息来增强监督学习机制中的初始内核。这些增强的内核是基于内核与附加特征内核的 Schur-Hadamard 乘积的简单更新方案构建的。作为正定核的生成器，我们将关注图基函数 (GBF)，它允许通过图傅立叶变换包含图的几何信息。使用用于机器学习的正则化最小二乘 (RLS) 方法，我们将测试派生的增强内核以对图上的数据进行分类。