Multi-view learning for high dimensional data classification

Facing to the high dimensional data, how to deal them well is the most difficult problem in the field of machine learning, pattern recognition and the relative fields. In this paper, we propose a new semi-supervised multi-view learning method, which partition or select the abundant attributes (called attribute partition or attribute selection) into subsets. We consider each subset as a view and on each subset train a classifier to label the unlabeled examples. Based on the ensemble learning, we combine their predictions to classify the unlabeled examples. The semi-supervised learning idea is that to make use of the large number unlabeled example to modify the classifiers iteratively. Experiments on UCI datasets show that this method is feasible and can improve the efficiency. Both theoretical analysis and experiments show that the proposed method has excellent accuracy and speed of classification.