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

Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1975-1983

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 \textit{active} query of informative samples by minimizing the expected prediction error, and (ii) an efficient {\it correction propagation} method that propagates human correction on selected samples over a {\it 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Su_2015_CVPR,
author = {Su, Hang and Yin, Zhaozheng and Kanade, Takeo and Huh, Seungil},
title = {Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}