Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph
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.
H. Su et al., "Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015, Boston, MA), vol. 7, pp. 1975-1983, IEEE Computer Society, Jun 2015.
The definitive version is available at http://dx.doi.org/10.1109/CVPR.2015.7298808
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015: Jun. 7-12, Boston, MA)
Keywords and Phrases
Classification (of Information); Computer Vision; Graphic Methods; Supervised Learning; Human Intervention; Manifold Structures; Prediction Errors; Propagation Algorithm; Propagation Method; Real-World Datasets; Semi-Supervised Learning Methods; Unlabeled Samples; Pattern Recognition
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International Standard Serial Number (ISSN)
Article - Conference proceedings
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