Search Results for author: Aamir Anis

Found 3 papers, 0 papers with code

A Sampling Theory Perspective of Graph-based Semi-supervised Learning

no code implementations26 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.

Graph Sampling

Asymptotic Justification of Bandlimited Interpolation of Graph signals for Semi-Supervised Learning

no code implementations14 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.

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

no code implementations16 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.

Active Learning

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