  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.
