Towards making co-training suffer less from insufficient views

Co-training is a famous semi-supervised learning algorithm which can exploit unlabeled data to improve learning performance. Generally it works under a two-view setting (the input examples have two disjoint feature sets in nature), with the assumption that each view is sufficient to predict the label. However, in real-world applications due to feature corruption or feature noise, both views may be insufficient and co-training will suffer from these insufficient views. In this paper, we propose a novel algorithm named Weighted Co-training to deal with this problem. It identifies the newly labeled examples that are probably harmful for the other view, and decreases their weights in the training set to avoid the risk. The experimental results show that Weighted Co-training performs better than the state-of-art co-training algorithms on several benchmarks.