Influence-Driven Data Poisoning in Graph-Based Semi-Supervised Classifiers. (arXiv:2012.07381v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2012.07381

Graph-based Semi-Supervised Learning (GSSL) is a practical solution to learn
from a limited amount of labelled data together with a vast amount of
unlabelled data. However, due to their reliance on the known labels to infer
the unknown labels, these algorithms are sensitive to data quality. It is
therefore essential to study the potential threats related to the labelled
data, more specifically, label poisoning. In this paper, we propose a novel
data poisoning method which efficiently approximates the result of …

arxiv data graph graph-based influence semi-supervised