A random subspace method for co-training

Semi-supervised learning has received much attention recently. Co-training is a kind of semi-supervised learning method which uses unlabeled data to improve the performance of standard supervised learning algorithms. A novel co-training style algorithm, RASCO (for RAndom Subspace CO-training), is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation. The accuracy and generalizability of RASCO are analyzed. The influences of the parameters of RASCO are discussed. Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms.