no code implementations • 26 May 2017 • Aamir Anis, Aly El Gamal, Salman Avestimehr, Antonio Ortega
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.
no code implementations • 14 Feb 2015 • Aamir Anis, Aly El Gamal, A. Salman Avestimehr, Antonio Ortega
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.
no code implementations • 16 May 2014 • Akshay Gadde, Aamir Anis, Antonio Ortega
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.