Graph-based semi-supervised learning with redundant views

In this paper, we propose a novel semi-supervised algorithm, which works under a two-view setting. Our algorithm, named kernel canonical component analysis graph (KC-GRAPH), can effectively enhance the performance and the parameter stability of traditional graph-based semi-supervised algorithms by taking the advantage of two views using kernel canonical component analysis (KCCA). Experiments have been presented for semi-supervised classification tasks, and have shown that our KC-GRAPH algorithm stays a high classification accuracy and is much more stable than the former algorithms. We also noticed that our algorithm holds very good parameter stability.