Abstract

When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flflawless classifification given a small number of initial annotations. To address this issue with minimal human interventions, we propose (i) a sample selection criterion used for active query of informative samples by minimizing the expected prediction error, and (ii) an effificient correction propagation method that propagates human correction on selected samples over a gradually-augmented graph to unlabeled samples without rebuilding the affifinity graph. Experimental results conducted on three real world datasets validate that our active sample selection and correction propagation algorithm quickly reaches high quality classi- fification results with minimal human interventions.