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arXiv · 1803.04193

Extreme Learning Machine for Graph Signal Processing

Arun Venkitaraman·Saikat Chatterjee·Peter Händel
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Abstract

In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.

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BibTeXRIS

Arun Venkitaraman, Saikat Chatterjee, Peter Händel. 2018-03-12. Extreme Learning Machine for Graph Signal Processing. https://arxiv.org/abs/1803.04193

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