首页|Model Change Active Learning in Graph-Based Semi-supervised Learning
Model Change Active Learning in Graph-Based Semi-supervised Learning
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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 iden-tify 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 clas-sifier by introducing the additional label(s).We pair this idea with graph-based semi-super-vised 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.
Active learningGraph-based methodsSemi-supervised learning(SSL)Graph Laplacian
Kevin S.Miller、Andrea L.Bertozzi
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Department of Mathematics,University of California,Los Angeles,520 Portola Plaza,Los Angeles,CA 90095,USA
DOD National Defense Science and Engineering Graduate(NDSEG)Research FellowshipNGANGA
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