Semi-Supervised Classification Based on Mixture Graph
AbstractGraph-based semi-supervised classification heavily depends on a well-structured graph. In this paper, we investigate a mixture graph and propose a method called semi-supervised classification based on mixture graph (SSCMG). SSCMG first constructs multiple k nearest neighborhood (kNN) graphs in different random subspaces of the samples. Then, it combines these graphs into a mixture graph and incorporates this graph into a graph-based semi-supervised classifier. SSCMG can preserve the local structure of samples in subspaces and is less affected by noisy and redundant features. Empirical study on facial images classification shows that SSCMG not only has better recognition performance, but also is more robust to input parameters than other related methods. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Feng, L.; Yu, G. Semi-Supervised Classification Based on Mixture Graph. Algorithms 2015, 8, 1021-1034.
Feng L, Yu G. Semi-Supervised Classification Based on Mixture Graph. Algorithms. 2015; 8(4):1021-1034.Chicago/Turabian Style
Feng, Lei; Yu, Guoxian. 2015. "Semi-Supervised Classification Based on Mixture Graph." Algorithms 8, no. 4: 1021-1034.