Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph

Author(s): Hang Su, Zhaozheng Yin, Takeo Kanade, and Seungil Huh
Date of Publication: June 2015
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 flawless classification 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 efficient correction propagation method that propagates human correction on selected samples over a gradually-augmented graph to unlabeled samples without rebuilding the affinity graph. Experimental results conducted on three real world datasets validate that our active sample selection and correction propagation algorithm quickly reaches high quality classification results with minimal human interventions.

Citation: Su, H., Yin, Z., Kanade, T., and Huh, S. Active sample selection and correction propagation on a gradually-augmented graph. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
Team(s): Plant Team