Robust graph-based image classifier learning with negative edge weights

Abstract

We study semi-supervised learning for image classifiers from a graph signal processing (GSP) perspective. Specifically, by viewing a binary classifier as a graph-signal in a high-dimensional feature space, we cast classifier learning as a signal restoration problem via a classical maximum a posteriori (MAP) formulation. Unlike previous graph-signal… (More)
DOI: 10.1109/ICME.2017.8019490

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Cite this paper

@article{Su2017RobustGI, title={Robust graph-based image classifier learning with negative edge weights}, author={Weng-Tai Su and Gene Cheung and Chia-Wen Lin}, journal={2017 IEEE International Conference on Multimedia and Expo (ICME)}, year={2017}, pages={397-402} }