Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning

• URL: http://arxiv.org/abs/2210.15786v1
• Date: Thu, 27 Oct 2022 22:07:53 GMT
• Title: Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning
• Authors: Kevin Miller and Jeff Calder
• Abstract summary: We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning. In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning (PWLL) for the classifier. We present experimental results on a number of graph-based image classification problems.
• Score: 1.6752182911522522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We show that uncertainty sampling is sufficient to achieve exploration versus exploitation in graph-based active learning, as long as the measure of uncertainty properly aligns with the underlying model and the model properly reflects uncertainty in unexplored regions. In particular, we use a recently developed algorithm, Poisson ReWeighted Laplace Learning (PWLL) for the classifier and we introduce an acquisition function designed to measure uncertainty in this graph-based classifier that identifies unexplored regions of the data. We introduce a diagonal perturbation in PWLL which produces exponential localization of solutions, and controls the exploration versus exploitation tradeoff in active learning. We use the well-posed continuum limit of PWLL to rigorously analyze our method, and present experimental results on a number of graph-based image classification problems.

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