Weakly supervised graph-based methods for classification
Dragomir Radev · 2004
We compare two weakly supervised graph-based classification algorithms: spectral partitioning and tripartite updating. We provide results from empirical tests on the problem of number classification. Our results indicate (a) that both methods require minimal labeled data, (b) that both methods scale well with the number of unlabeled examples, and (c) that tripartite updating outperforms spectral partitioning.