Model-Change Active Learning in Graph-Based Semi-Supervised Learning

• URL: http://arxiv.org/abs/2110.07739v1
• Date: Thu, 14 Oct 2021 21:47:10 GMT
• Title: Model-Change Active Learning in Graph-Based Semi-Supervised Learning
• Authors: Kevin Miller and Andrea L. Bertozzi
• Abstract summary: "Model-change" active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s) We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution.
• Score: 7.208515071018781
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. "Model-change" active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s). We pair this idea with graph-based semi-supervised learning methods, that use the spectrum of the graph Laplacian matrix, which can be truncated to avoid prohibitively large computational and storage costs. We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution. We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

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