ORIGINAL PAPERS

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

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  • Department of Mathematics, University of California, Los Angeles, 520 Portola Plaza, Los Angeles, CA 90095, USA

Received date: 2022-10-24

  Revised date: 2023-09-18

  Accepted date: 2023-09-21

  Online published: 2024-02-17

Supported by

This work was supported by the DOD National Defense Science and Engineering Graduate (NDSEG) Research Fellowship and the NGA under Contract No. HM04762110003.

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 (SSL) 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.

Cite this article

Kevin S. Miller, Andrea L. Bertozzi . Model Change Active Learning in Graph-Based Semi-supervised Learning[J]. Communications on Applied Mathematics and Computation, 2024 , 6(2) : 1270 -1298 . DOI: 10.1007/s42967-023-00328-z

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