We introduce semi-supervised data classification algorithms based on total variation (TV), Reproducing Kernel Hilbert Space (RKHS), support vector machine (SVM), Cheeger cut, labeled and unlabeled data points. We design binary and multi-class semi-supervised classification algorithms. We compare the TV-based classification algorithms with the related Laplacian-based algorithms, and show that TV classification perform significantly better when the number of labeled data is small.
@article{arxiv.1210.0699,
title = {TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification},
author = {Xavier Bresson and Ruiliang Zhang},
journal= {arXiv preprint arXiv:1210.0699},
year = {2012}
}