Multi-view semi-supervised web image classification via co-graph

We propose a new multi-view semi-supervised learning method named co-graph for web image classification. Co-graph combines multiple graphs together, each modeling the data points on one view, and enhances learners incrementally with co-training strategy by exploiting unlabeled data. Learners are locally co-trained in co-graph for the enhancement, i.e., only a part of local models in graphs, named dominant local models, need to be updated instead of the total. We also extend the co-graph algorithm to a general framework of local co-training over multiple graphs that is compatible with the common graph-based learning algorithms. Co-graph builds a bridge between graph-based methods and co-training, and contains the double label propagation: one propagates labels from labeled data to unlabeled data in each single view, and the other exchanges high-confidence label information across different views. Experimental results demonstrate the effectiveness of co-graph in web image classification.