Extreme Learning Machine for Graph Signal Processing
Machine Learning
2018-03-14 v1 Machine Learning
Signal Processing
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.
Cite
@article{arxiv.1803.04193,
title = {Extreme Learning Machine for Graph Signal Processing},
author = {Arun Venkitaraman and Saikat Chatterjee and Peter Händel},
journal= {arXiv preprint arXiv:1803.04193},
year = {2018}
}