Search Results for author:
Found 3 papers, 1 papers with code
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
In this work, we reinforce this connection by viewing the problem from a graph sampling theoretic perspective, where class indicator functions are treated as bandlimited graph signals (in the eigenvector basis of the graph Laplacian) and label prediction as a bandlimited reconstruction problem.
Asymptotic Justification of Bandlimited Interpolation of Graph signals for Semi-Supervised Learning
Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set.
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals
The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices.
