Semi-Supervised Learning on Graphs with Feature-Augmented Graph Basis Functions
This work addresses classification problems in graph-based semi-supervised learning, but it appears incremental as it builds on existing kernel methods with feature augmentation.
The paper tackles semi-supervised learning on graphs by augmenting kernels with additional features from priors or unsupervised outputs, using a simple update scheme based on the Schur-Hadamard product. It tests these augmented kernels with graph basis functions and regularized least squares for classification, but no concrete performance numbers are provided in the abstract.
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.