Multi-View Learning for High Dimensional Data Classification

摘要：

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.

关键词： Semi-supervised Multi-view learning Attribute Partition Attribute Selection and Ensemble learning

作者: Kunlun Li Xiaoqian Meng Zheng Cao Xue Sun

作者单位: College of Electronic and Information Engineering, Hebei University, Baoding 071002, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 3766-3770

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）