TV-SVM: Total Variation Support Vector Machine for Semi-Supervised Data Classification
This addresses classification problems in scenarios with limited labeled data, representing an incremental improvement over existing semi-supervised methods.
The paper tackled semi-supervised data classification by introducing algorithms based on total variation, RKHS, and SVM, showing that TV-based methods perform significantly better than Laplacian-based ones when labeled data is scarce.
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.