Data-Adaptive Active Sampling for Efficient Graph-Cognizant Classification

• Published in: IEEE transactions on signal processing Vol. 66; no. 19; pp. 5167 - 5179
• Main Authors: Berberidis, Dimitris, Giannakis, Georgios B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active learning; Adaptive sampling; Classification; Combinatorial analysis; Confidence; Correlation; Covariance matrices; expected change; graph-based; Graphical representations; Labels; Laplace equations; Markov chains; Minimization; Nodes; Numerical models; Optimality criteria; Optimization; Predictive models; Retraining; Sampling methods; Training

This paper deals with active sampling of graph nodes representing training data for binary classification. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that labels across neighboring nodes are corr...