Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification

Bo Wang, Zhuowen Tu, John K. Tsotsos; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 425-432

Abstract


In graph-based semi-supervised learning approaches, the classification rate is highly dependent on the size of the availabel labeled data, as well as the accuracy of the similarity measures. Here, we propose a semi-supervised multi-class/multi-label classification scheme, dynamic label propagation (DLP), which performs transductive learning through propagation in a dynamic process. Existing semi-supervised classification methods often have difficulty in dealing with multi-class/multi-label problems due to the lack in consideration of label correlation; our algorithm instead emphasizes dynamic metric fusion with label information. Significant improvement over the state-of-the-art methods is observed on benchmark datasets for both multiclass and multi-label tasks.

Related Material


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[bibtex]
@InProceedings{Wang_2013_ICCV,
author = {Wang, Bo and Tu, Zhuowen and Tsotsos, John K.},
title = {Dynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}